Search

Abstract Economic growth has brought about escalating environmental challenges. The purist of socioeconomic and environmental coordination necessitates a deep understanding of their interplay. Therefore, a game theory stepwise-cluster factorial coupling coordination model (GSFCM) has been developed to investigate the spatial and temporal patterns and driving forces of socioeconomic and environmental coordination in the Yangtze River Delta Region. Results reveal an upward in socioeconomic and environmental status from 2015 to 2020. All 41 cities in the region show progress towards coordination, coupled with spatial inequalities. Hangzhou, Shanghai, and Zhoushan demonstrate the highest coupling coordination degree (CCD), while Bozhou, Suzhou, and Fuyang located in Anhui exhibit the lowest levels. Socioeconomic factors contribute 82.98% to the enhancement of CCD, environmental factors contribute 11.05%, and their interaction accounts for 5.92%. Main drivers influencing CCD include Gross Domestic Product (GDP) per capita, the proportion of the added value of tertiary industry, PM2.5 concentration, and their interactions. Policy related to regional collaboration, industrial upgrades, and investment in environmental infrastructure are recomended to achieve a harmonious balance between economic growth and environmental protection. These insights are expected to support policy decisions in sustainable development of the region.

The occurrence of vaso-occlusive crisis greatly depends on the competition between the sickling delay time and the transit time of individual sickle cells, i.e., red blood cells from sickle cell disease (SCD) patients, while they are traversing the circulatory system. Many drugs for treating SCD work by inhibiting the polymerization of sickle hemoglobin (HbS), effectively delaying the sickling process in sickle cells (SS RBCs). Most previous studies on screening anti-sickling drugs, such as voxelotor, rely on in vitro testing of sickling characteristics, often conducted under prolonged deoxygenation for up to 1 hour. However, since the microcirculation of RBCs typically takes less than 1 minute, the results of these studies may be less accurate and less relevant for in vitro-in vivo correlation. In our current study, we introduce a computer vision-enhanced microfluidic framework designed to automatically capture the transient sickling kinetics of SS RBCs within a 1-min timeframe. Our study has successfully detected differences in the transient sickling kinetics between vehicle control and voxelotor-treated SS RBCs. This approach has the potential for broader applications in screening anti-sickling therapies.

304 stainless steel (304SS) and polyethylene terephthalate (PET) were lapped joined by laser conduction welding using a semiconductor laser with a flat-top thermal distribution. Experimental investigation and numerical simulation were conducted to analyze the weld morphology, pores distribution, interfacial microstructure, temperature field, joint strength and fracture behavior of the laser dissimilar joints. Because of the uniform thermal distribution of the laser, the decomposition of the PET base material was well controlled with pore-free joints obtaining at the welding speed over 25 mm/s. Besides, a compound layer was generated at the interface between 304SS and PET with its thickness decreased when the welding speed increased. Chemical reactions and mechanical anchoring were both observed at the interface suggesting a dual joining mechanism. The joint fracture load first increased then decreased with the increased welding speed. Besides, a ductile to brittle failure transition was clearly seen. The underlying mechanism was also discussed.

Abstract Communications between tumor cells and surrounding immune cells help shape the tumor immunity continuum. Recent breakthroughs in high-throughput technologies as well as computational algorithms had reported many important tumor-immune cell (TIC) communications, which were scattered in thousands of published studies and impeded systematical characterization of the TIC communications across cancer. Here, a comprehensive database, TICCom, was developed to model TIC communications, containing 739 experimentally-validated or manually-curated interactions collected from more than 3,000 literatures as well as 4,537,709 predicted interactions inferred via six computational algorithms by reanalyzing 32 scRNA-seq datasets and bulk RNA-seq data across 25 cancer types. The communications between tumor cells and 14 types of immune cells were characterized, and the involved ligand-receptor interactions were further integrated. 14190 human and 3650 mouse integrated ligand-receptor interactions with supplemented corresponding function information were also stored in the TICCom database. Our database would serve as a valuable resource for investigating TIC communications.

Abstract Cyclocarya paliurus has been widely applied for therapeutic feeding as a traditional Chinese medicine homologous food with high nutritional and medical value. Nevertheless, the specific bioactive compounds liable for hypoglycemic and hypolipidemic properties and underlying mechanisms remain unexplored. In the present study, the in vitro hypoglycemic and hypolipidemic effects of C. paliurus were assessed by α‐glucosidase and pancreatic lipase inhibition assays, and the results indicated that 70% ethanol extract exhibited remarkable α‐glucosidase and pancreatic lipase inhibitory activity with the IC50 value of 3.14 μg/mL and 0.98 μg/mL, respectively. A total of 18 and 15 potential ligand candidates were screened, and the chemical structures were characterized. Furthermore, the docking result showed that chlorogenic acid, quercetin, kaempferol 3‐O‐rhamnoside, and pterocaryoside A contributed to the underlying hypoglycemic and hypolipidemic effects of C. paliurus, further strengthened the bindings of the enzyme‐ligand complex and suppressed the enzyme's activity with the binding energies ranged from −4.00 to −6.31 kcal/mol. The interrelations between the specific bioactive constituents, α‐glucosidase or lipase, and hypoglycemic or hypolipidemic activities were also carified. The present study reveals substantial prospects of C. paliurus to be developed as a natural dietotherapy, disease‐prevention, and healthcare for diabetes and obesity in the near future.

Abstract In recent years, the emergence of soybean stay-green syndrome (SGS), also referred to as ‘zhengqing’, in the Huang-Huai-Hai region of China has resulted in significant yield losses. SGS is a phenomenon characterized by the delayed senescence of soybean, resulting in stay-green leaves, flat pods, and stunted seed development at harvest. We previously identified a distinct geminivirus, named soybean stay-green associated geminivirus (SoSGV), as the causative agent of SGS by fulfilling Koch’s postulates. To further understand the epidemiology of SoSGV, in this study, we collected 368 stay-green samples from 17 regions in 8 provinces including the Huang-Huai-Hai region and surrounding areas. The results showed that 228 samples tested positive for SoSGV (61.96%), and 96.93% of these positive samples showed severe pod deflation. Our epidemiological assessment reveals that SGS caused by the SoSGV is prevalent in the fields, and it is undergoing geographical expansion and genetic differentiation. Additionally, we determined other natural hosts grown in the Huang-Huai-Hai region. By capturing insects in the field and conducting laboratory vector transmission tests, we confirmed that the common brown leafhopper (Orosius orientalis) is the transmission vector of SoSGV. With a better understanding of the transmission and epidemiology of SoSGV, we can develop more effective strategies for managing and mitigating its impact on soybean yields.

Ti–6Al–4V alloys have been widely adopted because of their good mechanical properties. TIG welding is a common method for Ti–6Al–4V alloys. However, the high heat input of conventional TIG can lead to a coarsening of grains within Ti–6Al–4V and thus a weaker Ti–6Al–4V weld. This study presents a novel fast-frequency pulsed tungsten inert gas (FFP-TIG) welding technology to fabricate a high-reliability Ti–6Al–4V weld. The effects of fast-frequency pulsed waveforms on microstructures and mechanical properties of Ti–6Al–4V weld samples were uncovered. The arc core diameter of FFP-TIG technology was reduced by 32%–36% compared with that of conventional TIG. The adoption of FFP-TIG decreased the average grain sizes and acicular martensite α′ within the Ti–6Al–4V weld by 47.7% and 40.5%, respectively, which further enhanced the mechanical properties of the corresponding weldments. The hardness, tensile strength, and elongation of Ti–6Al–4V weldments obtained by FFP-TIG were improved by 10.35%, 13.84%, and 304.61% compared with those obtained by conventional TIG, achieving the highest value of 373 HV, 1018.97 MPa, and 4.301%, respectively. This study provides fresh insight into the development and application of high-reliability titanium alloy weldments commonly used in the aviation industry.

Central Asia is one of the most sensitive regions to climate changes in the world and the grassland degradation of this region has attracted considerable concern. Quantifying the driving force of grassland degradation is important for understanding the effects of climate variation and human activities on grassland. In this study, net primary productivity (NPP) was selected as an indicator to quantitatively evaluate the relative role of climate variation and human activities in Central Asia from 2000 to 2020. This study used the global NPP product MOD17A3 as actual NPP and estimated the potential NPP using the Thornthwaite memorial model. The potential NPP and the difference between the potential NPP and actual NPP were used to represent the influence of climate variation and human activities. The grassland degradation or restoration can be demonstrated by the slope of actual NPP (SA). A positive slope value (SA) suggested that restoration occurs, whereas a negative slope value suggested that degradation occurred. The results showed that 23.08% of the total grassland area experienced grassland degradation, whereas 2.51% of the whole grassland underwent grassland restoration. Furthermore, 53.8% of the degraded grassland areas were influenced by climate variation, and 14.5% were caused by human activities. By contrast, the relative roles of climate variation and human activities in grassland restoration were 25% and 47.9%, respectively. The NPP variation also could be calculated by assessing the effects of these factors and the results showed that 55.7% of the NPP decrease was caused by climate variation, whereas 9.6% was a result of human activities. On the contrary, climate variation and human activities resulted in 19.8% and 37.3% of grassland restoration, respectively. Therefore, climate variation was the dominant factor of grassland degradation, and human activities were the main driver of grassland restoration in Central Asia.

We overview several properties—old and new—of training overparameterized deep networks under the square loss. We first consider a model of the dynamics of gradient flow under the square loss in deep homogeneous rectified linear unit networks. We study the convergence to a solution with the absolute minimum ρ, which is the product of the Frobenius norms of each layer weight matrix, when normalization by Lagrange multipliers is used together with weight decay under different forms of gradient descent. A main property of the minimizers that bound their expected error for a specific network architecture is ρ. In particular, we derive novel norm-based bounds for convolutional layers that are orders of magnitude better than classical bounds for dense networks. Next, we prove that quasi-interpolating solutions obtained by stochastic gradient descent in the presence of weight decay have a bias toward low-rank weight matrices, which should improve generalization. The same analysis predicts the existence of an inherent stochastic gradient descent noise for deep networks. In both cases, we verify our predictions experimentally. We then predict neural collapse and its properties without any specific assumption—unlike other published proofs. Our analysis supports the idea that the advantage of deep networks relative to other classifiers is greater for problems that are appropriate for sparse deep architectures such as convolutional neural networks. The reason is that compositionally sparse target functions can be approximated well by “sparse” deep networks without incurring in the curse of dimensionality.

IntroductionOptical Coherence Tomography Angiography (OCTA) is a new non-invasive imaging modality that gains increasing popularity for the observation of the microvasculatures in the retina and the conjunctiva, assisting clinical diagnosis and treatment planning. However, poor imaging quality, such as stripe artifacts and low contrast, is common in the acquired OCTA and in particular Anterior Segment OCTA (AS-OCTA) due to eye microtremor and poor illumination conditions. These issues lead to incomplete vasculature maps that in turn makes it hard to make accurate interpretation and subsequent diagnosis.MethodsIn this work, we propose a two-stage framework that comprises a de-striping stage and a re-enhancing stage, with aims to remove stripe noise and to enhance blood vessel structure from the background. We introduce a new de-striping objective function in a Stripe Removal Net (SR-Net) to suppress the stripe noise in the original image. The vasculatures in acquired AS-OCTA images usually exhibit poor contrast, so we use a Perceptual Structure Generative Adversarial Network (PS-GAN) to enhance the de-striped AS-OCTA image in the re-enhancing stage, which combined cyclic perceptual loss with structure loss to achieve further image quality improvement.Results and discussionTo evaluate the effectiveness of the proposed method, we apply the proposed framework to two synthetic OCTA datasets and a real AS-OCTA dataset. Our results show that the proposed framework yields a promising enhancement performance, which enables both conventional and deep learning-based vessel segmentation methods to produce improved results after enhancement of both retina and AS-OCTA modalities.

Exploring the impacts of human disturbance on ecosystem services (ESs) in karst areas is crucial for regional sustainable management, and China’s ecological conservation red lines (ECRLs) could provide innovative solutions for global biodiversity conservation. However, it remains unclear how the implementation of ECRLs policies will change the mechanism of human disturbance on ESs. Based on the Conversion of Land Use and its Effects at Small Region Extent (CLUE-S) model, we projected land use changes in 2035 under a Business as Usual (BAU) scenario, Ecological Protection (ELP) scenario, and Future City Plan (FCP) scenario in Weining County, China. The Integrated Valuation of Ecosystem Services and Trade-off (InVEST) model was used to quantify six ESs—crop production, water yield, soil conservation, carbon storage, nitrogen export, habitat quality—and total ecosystem services (TES). Then, we analyzed the impacts of human disturbance changes on ESs. Our research showed that under the ELP scenario, the human disturbance index would decrease by 6.18% and increase in soil conservation (0.34%), carbon storage (21.27%), and habitat quality (9.14%) compared with 2015. A greater area of forest and grassland would lead to a reduction in cropland area, which would lower crop production by 20.76%, with water yield and nitrogen export decreasing by 6.63% and 19.91%, respectively, from 2015 to 2035. The ELP scenario would lessen trade-offs between water yield and carbon storage and habitat quality, such that water yield and nitrogen export would be similar. Meanwhile, to ensure future crop production, more attention should be given to the increase in water yield and nitrogen export caused by cropland expansion, especially in its non-karst areas. TES would increase by 9.27%, which is higher than that obtained under either the BAU or FCP scenario. Eco-environmental conservation and restoration policies were proposed to help its regional high-quality development. Our research provides a decision-making basis to evaluate the implementation effects of ECRLs policies in karst areas and inform the directed management of ecosystems by the government.

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

A new method for simultaneous determination of puerarin, daidzin, daidzein and genistein in Radix puerariae by ultra-high performance liquid chromatography was established. The target analytes were extracted from Radix puerariae by 70% ethylene glycol with the assistance of ultrasonication, purified by the absorption of N-propyl ethylenediamine (PSA), and separated on a Supersil ODS column (4.6 mm × 250 mm × 2.5 μm). Gradient elution in 12 min was performed with the mobile phase 0.1% formic acid(A)–acetonitrile(B). The column temperature was 25 °C and the flow rate was 1 mL/min. The detection wavelength of the four target analytes was 250 nm. The limits of detection (LODs) of puerarin, daidzin, daidzein and genistein were 0.086 mg/L, 0.020 mg/L, 0.027 mg/L and 0.037 mg/L, respectively, and limits of quantitation (LOQs) were 0.29 mg/L, 0.065 mg/L, 0.090 mg/L and 0.12 mg/L, respectively. The recovery of the four substances ranged from 90.5% to 109.6%, and the relative standard deviation (n = 6) was less than 7.7%. With the established methods, puerarin, daidzin, daidzein and genistein in Radix puerariae from 11 origins were determined. The contents of the four compounds varied with the origin and variety. It provides basic data and technical means for quality control and regulation of Radix puerariae.

The in vitro digestion and fermentation behaviors of Lactobacillus helveticus LZ-R-5- and L. pentosus LZ-R-17-sourced exopolysaccharides (LHEPS and LPEPS) were investigated by stimulated batch-culture fermentation system. The results illustrated that LHEPS was resistant to simulated saliva and gastrointestinal (GSI) digestion, whereas LPEPS generated a few monosaccharides after digestion without significant influence on its main structure. Additionally, LHEPS and LPEPS could be consumed by the human gut microbiota and presented stronger bifidogenic effect comparing to α-glucan and β-glucan, as they promote the proliferation of Lactobacillus and Bifidobacterium in cultures and exhibited high values of selectivity index (13.88 and 11.78, respectively). Furthermore, LPEPS achieved higher contents of lactic acid and acetic acid (35.74 mM and 45.91 mM, respectively) than LHEPS (35.20 mM and 44.65 mM, respectively) during fermentation for 48 h, thus also resulting in a larger amount of total SCFAs (110.86 mM). These results have clearly indicated the potential prebiotic property of EPS fractions from L. helveticus LZ-R-5 and L. pentosus LZ-R-17, which could be further developed as new functional food prebiotics to beneficially improve human gut health.

A new rapid and accurate method was developed for simultaneous determination of pyridoxine and pyridoxal in ginkgo seeds, using ultra-performance liquid chromatography (UPLC) equipped with a fluorescence detector. Diluted hydrochloric acid solution was used as the extracting solvent. For the pretreatment of extracts, a zeolitic imidazolate framework material (ZIF-8) was prepared and characterized. An ODS-BP column (4.6 mm × 250 mm × 5 μm) was used for separation. The conditions of sample extraction, cleaning and separation were optimized. The linear correlation coefficient (R2) of the analyte was better than 0.9999, indicating good linearity. The limits of detection (LODs) of pyridoxal and pyridoxine were 0.0065 mg/kg and 0.0057 mg/kg, respectively, and limits of quantitation (LOQs) were 0.022 mg/kg and 0.019 mg/kg, respectively. The recovery of the two substances ranged from 86.2% to 110.4%, and the relative standard deviation (n = 6) was less than 7.5%. The method was applied to determine the contents of pyridoxine and pyridoxal in actual ginkgo seed samples with satisfactory results.

Identifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimer's disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a quantitative method for functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration. An important finding of our study is the ability of the new method to capture subtle changes for individual patients before and after short-term intervention. More broadly, the MG2G method can be used in studying multiple brain disorders and injuries, e.g., in Parkinson's disease or traumatic brain injury (TBI), and hence it will be useful to the wider neuroscience community.

Abstract Background China is experiencing environmental issues and related health effects due to its industrialization and urbanization. The health effects associated with air pollution are not just a matter of epidemiology and environmental science research, but also an important social science issue. Literature about the relationship of socioeconomic factors with the environment and health factors is inadequate. The relationship between air pollution exposure and health effects in China was investigated with consideration of the socioeconomic factors. Methods Based on nationwide survey data of China in 2014, we applied the multilevel mixed-effects model to evaluate how socioeconomic status (represented by education and income) contributed to the relationship between self-rated air pollution and self-rated health status at community level and individual level. Results The findings indicated that there was a non-linear relationship between the community socioeconomic status and community air pollution in urban China, with the highest level of air pollution presented in the communities with moderate socioeconomic status. In addition, health effects associated air pollution in different socioeconomic status groups were not equal. Self-rated air pollution had the greatest impact on self-rated health of the lower socioeconomic groups. With the increase of socioeconomic status, the effect of self-rated air pollution on self-rated health decreased. Conclusions This study verified the different levels of exposure to air pollution and inequality in health effects among different socioeconomic groups in China. It is imperative for the government to urgently formulate public policies to enhance the ability of the lower socioeconomic groups to circumvent air pollution and reduce the health damage caused by air pollution.

In this study, a high-throughput sequencing technique was used to analyze bacterial and fungal diversity of two traditional Tibetan kefir grains from Linzhi (K1) and Naqu (K2) regions. Comparative bioinformatic analyses indicated that Lactobacillus kefiranofaciens, L. kefiri and Kluyveromyces marxianus were the main dominant strains in K1 and K2. In order to research the relationship of the growth of kefir grains, the biofilm and the extracellular polysaccharides (EPS) produced by microorganisms, the proliferation rate of kefir grains, the yield and chemical structure of EPS and the optimal days for biofilm formation were determined. The results showed that the growth rate, the yield of EPS and the biofilm formation ability of K1 were higher than K2, and the optimal day of their biofilm formation was the same in 10th day. Additionally, the live cells, dead cells and EPS in biofilm formation of K1 and K2 were observed by fluorescence microscope to clarify the formation process of kefir grains. To determine the influence of microbial interactions on biofilm and the formation of kefir grains, the essential role of microbial quorum sensing needs further attention.

Sickle cell disease (SCD) is a hematological disorder leading to blood vessel occlusion accompanied by painful episodes and even death. Red blood cells (RBCs) of SCD patients have diverse shapes that reveal important biomechanical and bio-rheological characteristics, e.g. their density, fragility, adhesive properties, etc. Hence, having an objective and effective way of RBC shape quantification and classification will lead to better insights and eventual better prognosis of the disease. To this end, we have developed an automated, high-throughput, ex-vivo RBC shape classification framework that consists of three stages. First, we present an automatic hierarchical RBC extraction method to detect the RBC region (ROI) from the background, and then separate touching RBCs in the ROI images by applying an improved random walk method based on automatic seed generation. Second, we apply a mask-based RBC patch-size normalization method to normalize the variant size of segmented single RBC patches into uniform size. Third, we employ deep convolutional neural networks (CNNs) to realize RBC classification; the alternating convolution and pooling operations can deal with non-linear and complex patterns. Furthermore, we investigate the specific shape factor quantification for the classified RBC image data in order to develop a general multiscale shape analysis. We perform several experiments on raw microscopy image datasets from 8 SCD patients (over 7,000 single RBC images) through a 5-fold cross validation method both for oxygenated and deoxygenated RBCs. We demonstrate that the proposed framework can successfully classify sickle shape RBCs in an automated manner with high accuracy, and we also provide the corresponding shape factor analysis, which can be used synergistically with the CNN analysis for more robust predictions. Moreover, the trained deep CNN exhibits good performance even for a deoxygenated dataset and distinguishes the subtle differences in texture alteration inside the oxygenated and deoxygenated RBCs.

The effect of bonding temperature on the microstructure and mechanical properties of transient liquid phase (TLP) joints of GH4169 superalloy was investigated. Joining processes were carried out at 1040–1100 °C for 30 min using BNi-2 solder paste. The results showed that three distinct microstructural zones were formed in the joint region: an athermal solidification zone (ASZ), consisting of eutectic compounds; an isothermal solidification zone (ISZ), consisting of γ solid solution; and a diffusion affected zone (DAZ), consisting of Ni-Cr rich boride and Cr-Nb-Mo-rich boride compounds. With increasing bonding temperature, the amounts of eutectic compounds in ASZ first decreased and then increased. A eutectic-free joint centerline was obtained at 1080 °C. The maximum bonding shear strength reached 728.03 MPa due to the completion of isothermal solidification. Fractographic studies revealed that the boride compounds in ASZ and the intermetallic compounds in DAZ were the main causes for the failure of joints. The fracture mode of the sample bonded at 1040 °C was brittle, and the fracture path was along the ASZ. However, the fracture mode of the sample bonded at 1080 °C was ductile, and the fracture occurred along the DAZ.

The Ni-based coatings were deposited on the 1Cr10Mo1NiWVNbN steel by using laser cladding process. The microstructure and properties of the coatings interface area were investigated by OM (Optical Microscopy), SEM (Scanning Electron Microscope), XRD (X-Ray Diffraction) microhardness test and EDS (Energy Spectrum Analysis) analysis. The results show that the bonding condition of the coatings interface is different in the monolayer and the trilayer. The monolayer coatings have a small dilution area. The dilution rate in a coating layer increases by layers. The scale of ferrite (α) phase increases with the layer increases. The surface cladding quality of a monolayer is better than that of the trilayer coatings. The width of the interface increases with the increase of the layer. The width of the interface region in the trilayer coatings increases significantly. The microhardness of the interface zone is much higher than that in the coatings zone and the substrate zone. The microhardness of trilayer coatings is higher than that of the monolayer.

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space-time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems. [ABSTRACT FROM AUTHOR]

Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from the high computational cost and excessive memory requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks. Graph embedding techniques can be effective in converting high-dimensional sparse graphs into low-dimensional, dense, and continuous vector spaces, preserving maximally the graph structure properties. Another type of emerging graph embedding employs Gaussian distribution--based graph embedding with important uncertainty estimation. The main goal of graph embedding methods is to pack every node's properties into a vector with a smaller dimension; hence, node similarity in the original complex irregular spaces can be easily quantified in the embedded vector spaces using standard metrics. The nonlinear and highly informative graph embeddings generated in the latent space can be conveniently used to address different downstream graph analytics tasks (e.g., node classification, link prediction, community detection, visualization, etc.). In this review, we present some fundamental concepts in graph analytics and graph embedding methods, focusing in particular on random walk--based and neural network--based methods. We also discuss the emerging deep learning--based dynamic graph embedding methods. We highlight the distinct advantages of graph embedding methods in four diverse applications, and we present implementation details and references to open-source software as well as available databases in the supplementary material to help interested readers start their exploration into graph analytics. [ABSTRACT FROM AUTHOR]

IntroductionIn contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors.MethodsFor automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species.ResultsThese models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties.DiscussionThis research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species.

The light chains (KLCs) of the microtubule motor kinesin-1 bind cargoes and regulate its activity. Through their tetratricopeptide repeat domain (KLCTPR), they can recognize short linear peptide motifs found in many cargo proteins characterized by a central tryptophan flanked by aspartic/glutamic acid residues (W-acidic). Using a fluorescence resonance energy transfer biosensor in combination with X-ray crystallographic, biochemical, and biophysical approaches, we describe how an intramolecular interaction between the KLC2TPR domain and a conserved peptide motif within an unstructured region of the molecule, partly occludes the W-acidic binding site on the TPR domain. Cargo binding displaces this interaction, effecting a global conformational change in KLCs resulting in a more extended conformation. Thus, like the motor-bearing kinesin heavy chains, KLCs exist in a dynamic conformational state that is regulated by self-interaction and cargo binding. We propose a model by which, via this molecular switch, W-acidic cargo binding regulates the activity of the holoenzyme. [ABSTRACT FROM AUTHOR]

Permutation entropy (PE) is a novel measure to quantify the complexity of nonlinear time series. In this paper, we propose a generalized permutation entropy (PEq,δ) based on the recently postulated entropic form, Sq,δ, which was proposed as an unification of the well-known Sq of nonextensivestatistical mechanics and Sδ, a possibly appropriate candidate for the black-hole entropy. We find that PEq,δ with appropriate parameters can amplify minor changes and trends of complexities in comparison to PE. Experiments with this generalized permutation entropy method are performed with both synthetic and stock data showing its power. Results show that PEq,δ is an exponential function of q and the power (k(δ) is a constant if δ is determined. Some discussions about k(δ) are provided. Besides, we also find some interesting results about power law. [ABSTRACT FROM AUTHOR]

Permutation entropy (PE) is a novel measure to quantify the complexity of nonlinear time series. In this paper, we propose a generalized permutation entropy (PEq,δ) based on the recently postulated entropic form, Sq,δ, which was proposed as an unification of the well-known Sq of nonextensivestatistical mechanics and Sδ, a possibly appropriate candidate for the black-hole entropy. We find that PEq,δ with appropriate parameters can amplify minor changes and trends of complexities in comparison to PE. Experiments with this generalized permutation entropy method are performed with both synthetic and stock data showing its power. Results show that PEq,δ is an exponential function of q and the power (k(δ) is a constant if δ is determined. Some discussions about k(δ) are provided. Besides, we also find some interesting results about power law. [ABSTRACT FROM AUTHOR]

In this study, we develop a new algorithm based on fractional operators of variable-order in order to enhance image quality. First, three kinds of popular high-order discrete formulas are adopted to obtain the coefficients, and subsequently, a mask optimization method for selecting the fractional order adaptively is applied to construct a variable-order fractional differential mask along with the coefficients generated from the first step. We carry out experiments on OCT thoracic aorta images and some nature images with low contrast and noise, demonstrating that the high-order discrete method leads to significantly better performance in enhancing the edge information nonlinearly compared to the standard first-order discrete method. Moreover, the optimized mask with variable-order of the fractional derivative not only can preserve the edge information of the processed images adequately, but it also effectively suppresses the noise in the smooth area. [ABSTRACT FROM AUTHOR]

Traditional Hessian multiscale filter consider only the local geometric feature but not the global grayscale information. In medical image analysis, Hessian filter is usually used to enhance the blood vessels. However, it also produces some pseudo vascular structures or some isolate noise points, such as the nasal soft tissues that have the similar shape with the vessels in MRA data, which will increase the difficulty of cerebrovascular segmentation. To resolve this issue, an improved Hessian multiscale filter is proposed in this paper. An image grayscale factor is added to the vascular similarity function computed by Hessian matrix eigenvalue. This method is experimented on brain MRA data and lung CTA data. Experimental results show that this method can enhance vascular structures, and simultaneously reduce the appearance of the pseudo vascular structures and the isolated noise points. [ABSTRACT FROM AUTHOR]