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Abstract High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.

Abstract Quantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search capability. How to balance the high price of quantum computing resources and the growing computing needs has become an urgent problem to be solved. We propose a novel optimization strategy based on an active learning scheme that combines the Quantum-inspired Genetic Algorithm (QGA) with machine learning surrogate model regression. Using Random Forests as the surrogate model circumvents the time-consuming physical modeling or experiments, thereby improving the optimization efficiency. QGA, a genetic algorithm embedded with quantum mechanics, combines the advantages of quantum computing and genetic algorithms, enabling faster and more robust convergence to the optimum. Using the design of planar multilayer photonic structures for transparent radiative cooling as a testbed, we show superiority of our algorithm over the classical genetic algorithm (CGA). Additionally, we show the precision advantage of the Random Forest (RF) model as a flexible surrogate model, which relaxes the constraints on the type of surrogate model that can be used in other quantum computing optimization algorithms (e.g., quantum annealing needs Ising model as a surrogate).

Abstract The oxide and halide perovskite materials with a ABX3 structure exhibit a number of excellent properties, including a high dielectric constant, electrochemical properties, a wide band gap, and a large absorption coefficient. These properties have led to a range of applications, including renewable energy and optoelectronics, where high-performance catalysts are needed. However, it is difficult for a single structure of perovskite alone to simultaneously fulfill the diverse needs of multiple applications, such as high performance and good stability at the same time. Consequently, perovskite nanocomposites have been developed to address the current limitations and enhance their functionality by combining perovskite with two or more materials to create complementary materials. This review paper categorizes perovskite nanocomposites according to their structural composition and outlines their synthesis methodologies, as well as their applications in various fields. These include fuel cells, electrochemical water splitting, CO2 mitigation, supercapacitors, and optoelectronic devices. Additionally, the review presents a summary of their research status, practical challenges, and future prospects in the fields of renewable energy and electronics. Graphical abstract

Abstract Background Clear cell renal cell carcinoma (ccRCC) is one of the most common urological diseases and the most common subtype of renal cell carcinoma. Nuclear mitochondria‐related genes (MTRGs) play an essential role in cancer, but their effect on ccRCC has not been clarified. This work aimed to investigate the role of nuclear MTRGs in ccRCC. Methods We collected nuclear MTRGs from the MITOMAP database and obtained the ccRCC profile from the TCGA database. Gene expression validation came from the GEO database. The ccRCC subtypes were determined by unsupervised clustering analysis based on the nuclear MTRGs. Immune scores were computed using the EPIC algorithm. The drug sensitivity scores were calculated using GDSC resources. A nomogram explored the diagnostic value of the nuclear MTRGs. Results In total, 11 nuclear MTRGs were identified as both related to prognosis and differentially expressed in ccRCC, showing a significant positive correlation with CD274 expression. We determined two subtypes of ccRCC based on these genes and found remarkable differences in survival status, immune infiltration, mutation landscape, and drug sensitivity between the two subtypes. The high‐MTRG group had a better prognosis and a lower tumor stage than the low‐MTRG group. Immune checkpoint blockade therapy was more effective for the high‐MTRG group. A nomogram based on the nuclear MTRGs concluded that patients with a higher score had poorer survival. SUCLA2 (succinate‐CoA ligase ADP‐forming subunit beta) was identified as the hub gene linked to ccRCC. High SUCLA2 expression showed a correlation with better survival and a negative correlation with the tumor mutation burden in ccRCC. Pan‐cancer analysis revealed wide‐ranging roles for SUCLA2 across human tumors. Conclusions Nuclear MTRGs play vital roles in determining the molecular features, immune infiltration, and drug sensitivity of ccRCC. High levels of nuclear MTRGs may indicate a better prognosis for patients with ccRCC. SUCLA2 is a representative nuclear MTRG and may serve as a protective biomarker in ccRCC. Our study provides therapeutic guidance and potential biomarkers for ccRCC patients, and contributes to the advancement of precision medicine.

Aspergillus fumigatus spondylitis is a rare fungal infection, primarily occurring in immunocompromised patients, although cases in immunocompetent individuals have also been reported. While Aspergillus fumigatus is commonly associated with pulmonary infections, it can also cause spondylitis. Patients typically present with back pain, limb numbness, and neurological compression symptoms. Imaging findings often show vertebral destruction, reduced disk height, and paraspinal abscesses, potentially accompanied by characteristic ring-enhancing lesions. MRI findings can help distinguish Aspergillus fumigatus spondylitis from other conditions such as tuberculous spondylitis. This case involves an immunocompetent patient with Aspergillus fumigatus spondylitis, whose non-specific clinical manifestations can easily be confused with other types of spinal infections, leading to a potential misdiagnosis. Diagnosis requires tissue biopsy and microbiological culture. Voriconazole is the first-line antifungal agent, and studies have shown that it improves patient response and survival rates. For patients with significant spinal compression or neurological symptoms, surgical intervention combined with antifungal treatment should be considered if antifungal therapy alone is ineffective. Although Aspergillus fumigatus spondylitis is rare, it can occur in immunocompetent individuals. Early diagnosis through imaging and biopsy is crucial, and a combination of surgery and antifungal therapy can help improve prognosis.

Introduction The co-development of public–private hospitals in China’s healthcare system significantly influences the system’s service utilisation, but the public–private hospital interaction is poorly understood. Bridging this knowledge gap is crucial for informed policy-making and promoting a more balanced healthcare system. This study measures China’s public–private hospital co-development and the impact of provincial and regional economic development levels on public–private hospital co-development.Methods We use service utilisation, a comprehensive measure of resource allocation, service capacity and service efficiency, to assess the co-development of China’s public and private hospitals. Using the coupling coordination degree model, a novel approach borrowed from physics, we measured the degree of interaction between the public and private pillars of China’s hospital system. Second, we measured the relationship between the degree of private–public hospital interaction and the level of provincial–regional economic development, including provincial direct and spillover effects. Data were collected on 2011–2021 service utilisation across 31 Chinese provinces. Fixed-effect models and the spatial Durbin model estimated the direct and spillover effects between the coupling coordination degree and the level of provincial economic development.Results We found that national, regional and provincial service utilisation in private hospitals expanded, while service utilisation remained constant in public hospitals. China’s private–public hospital co-development improved from the ‘barely co-developed’ (0.696 in 2011) to the ‘partially co-developed’ level (0.752 in 2021) in the public–private transitional co-development stage. We identified significant regional heterogeneity, with the eastern region co-development slightly better than the central region and both substantially better than the western region. There was also significant provincial heterogeneity. Increases in regional and provincial economic development promoted the service utilisation of private hospitals, improving the balanced co-development of public–private hospitals in the home and adjacent provinces (p

Inspired by Model Predictive Interaction Control (MPIC), this paper proposes differential models for estimating contact geometric parameters and normal-friction forces and formulates an optimal control problem with multiple constraints to allow robots to perform rigid–soft heterogeneous contact tasks. Within the MPIC, robot dynamics are linearized, and Extended Kalman Filters are used for the online estimation of geometry-aware parameters. Meanwhile, a geometry-aware Hertz contact model is introduced for the online estimation of contact forces. We then implement the force-position coordinate optimization by incorporating the contact parameters and interaction force constraints into a gradient-based optimization MPC. Experimental validations were designed for two contact modes: “single-point contact” and “continuous contact”, involving materials with four different Young’s moduli and tested in human arm “relaxation–contraction” task. Results indicate that our framework ensures consistent geometry-aware parameter estimation and maintains reliable force interaction to guarantee safety. Our method reduces the maximum impact force by 50% and decreases the average force error by 42%. The proposed framework has potential applications in medical and industrial tasks involving the manipulation of rigid, soft, and deformable objects.

Background: Spondylitis is a spinal infection which has been increasing in susceptible populations globally. This disease is caused by various microorganisms. Fungal spondylitis is rare in clinical practice and is strongly associated with immunosuppression and diabetes. Here, we report a case of suspected ca Melampsora spondylitis. Case presentation: A patient was suspected with Melampsora spondylitis at the L3-S1 level. The patient received two surgical operations and antifungal treatments. The next-generation sequencing (NGS) analysis of the tissue specimen obtained during the two surgical procedures confirmed the diagnosis of Melampsora spondylitis. The patient was successfully treated with voriconazole, vancomycin, and meropenem following surgical debridement with pedicle screw internal fixation. Conclusion: The diagnosis of fungal spondylitis is often delayed or missed. Physicians should consider fungal spondylitis in the differential diagnosis of neck pain to facilitate early treatment and prevent spinal cord injury and disability. Although fungal infections often occur in immunocompromised patients, fungal spondylitis has also been reported in immunocompetent patients in recent years. In addition, Candida albicans is usually considered a common bacterium in fungal spondylitis. This case underscores the need to develop more advanced diagnostic and therapeutic techniques to identify the pathogenic bacteria associated with fungal spondylitis besides Candida albicans.

Cytotoxic CD8+ T lymphocytes (CTLs) have been implicated in the severity of COVID-19. The TCR-pMHC ternary complex, formed by the T cell receptor (TCR) and peptide-MHC (major histocompatibility complex), constitutes the molecular basis of CTL responses against SARS-CoV-2. While numerous studies have been conducted on T cell immunity, the molecular mechanisms underlying CTL-mediated immunity against SARS-CoV-2 infection have not been well elaborated. In this review, we described the association between HLA variants and different immune responses to SARS-CoV-2 infection, which may lead to varying COVID-19 outcomes. We also summarized the specific TCR repertoires triggered by certain SARS-CoV-2 CTL epitopes, which might explain the variations in disease outcomes among different patients. Importantly, we have highlighted the primary strategies used by SARS-CoV-2 variants to evade T-cell killing: disrupting peptide-MHC binding, TCR recognition, and antigen processing. This review provides valuable insights into the molecule mechanism of CTL responses during SARS-CoV-2 infection, aiding efforts to control the pandemic and prepare for future challenges.

Pulmonary fibrosis is a progressive interstitial lung disease associated with aging. The pathogenesis of pulmonary fibrosis remains unclear, however, alveolar epithelial cell injury, myofibroblast activation, and extracellular matrix (ECM) accumulation are recognized as key contributors. Moreover, recent studies have implicated cellular senescence, endothelial-mesenchymal transition (EndMT), and epigenetic modifications in the pathogenesis of fibrotic diseases. Various signaling pathways regulate pulmonary fibrosis, including the TGF-β, Notch, Wnt, Hedgehog, and mTOR pathways. Among these, the TGF-β pathway is extensively studied, while the Notch pathway has emerged as a recent research focus. The Notch pathway influences the fibrotic process by modulating immune cell differentiation (e.g., macrophages, lymphocytes), inhibiting autophagy, and promoting interstitial transformation. Consequently, inhibiting Notch signaling represents a promising approach to mitigating pulmonary fibrosis. In this review, we discuss the role of Notch signaling pathway in pulmonary fibrosis, aiming to offer insights for future therapeutic investigations.

Idiopathic pulmonary fibrosis (IPF) is a prevalent chronic pulmonary fibrosis disease characterized by alveolar epithelial cell damage, fibroblast proliferation and activation, excessive extracellular matrix deposition, and abnormal epithelial-mesenchymal transition (EMT), resulting in tissue remodeling and irreversible structural distortion. The mortality rate of IPF is very high, with a median survival time of 2–3 years after diagnosis. The exact cause of IPF remains unknown, but increasing evidence supports the central role of epigenetic changes, particularly microRNA (miRNA), in IPF. Approximately 10% of miRNAs in IPF lung tissue exhibit differential expression compared to normal lung tissue. Diverse miRNA phenotypes exert either a pro-fibrotic or anti-fibrotic influence on the progression of IPF. In the context of IPF, epigenetic factors such as DNA methylation and long non-coding RNAs (lncRNAs) regulate differentially expressed miRNAs, which in turn modulate various signaling pathways implicated in this process, including transforming growth factor-β1 (TGF-β1)/Smad, mitogen-activated protein kinase (MAPK), and phosphatidylinositol-3-kinase/protein kinase B (PI3K/AKT) pathways. Therefore, this review presents the epidemiology of IPF, discusses the multifaceted regulatory roles of miRNAs in IPF, and explores the impact of miRNAs on IPF through various pathways, particularly the TGF-β1/Smad pathway and its constituent structures. Consequently, we investigate the potential for targeting miRNAs as a treatment for IPF, thereby contributing to advancements in IPF research.

Abstract Profit distribution in sequential innovation is a crucial yet relatively unexplored area of empirical research. With novel cross-section data from the game live-streaming industry, we are the first to assess the contribution shared by initial and follow-on innovators empirically. Unlike the complex innovation paths of patents, the copyright structure within the game live-streaming industry is clear and concise, enabling direct measurement of innovation value. At the industry’s average level, the share stands at 1:2 between game producers and streamers. This measurement remains robust even after controlling for income levels and distinguishing between professionals and amateurs. Nevertheless, significant heterogeneity exists across different game genres. We suggest that a balanced distribution scheme between initial and follow-on innovators should seriously consider their contribution shares.

Abstract NLRP inflammasomes are a group of cytosolic multiprotein oligomer pattern recognition receptors (PRRs) involved in the recognition of pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs) produced by infected cells. They regulate innate immunity by triggering a protective inflammatory response. However, despite their protective role, aberrant NLPR inflammasome activation and gain-of-function mutations in NLRP sensor proteins are involved in occurrence and enhancement of non-communicating autoimmune, auto-inflammatory, and neurodegenerative diseases. In the last few years, significant advances have been achieved in the understanding of the NLRP inflammasome physiological functions and their molecular mechanisms of activation, as well as therapeutics that target NLRP inflammasome activity in inflammatory diseases. Here, we provide the latest research progress on NLRP inflammasomes, including NLRP1, CARD8, NLRP3, NLRP6, NLRP7, NLRP2, NLRP9, NLRP10, and NLRP12 regarding their structural and assembling features, signaling transduction and molecular activation mechanisms. Importantly, we highlight the mechanisms associated with NLRP inflammasome dysregulation involved in numerous human auto-inflammatory, autoimmune, and neurodegenerative diseases. Overall, we summarize the latest discoveries in NLRP biology, their forming inflammasomes, and their role in health and diseases, and provide therapeutic strategies and perspectives for future studies about NLRP inflammasomes.

Pharmacogenetic testing technology can effectively determine the individual differences among patients and scientifically assist doctors to select the most suitable drugs for each patient. In this study, an electrochemical biosensor based on magnetic α-Fe2O3/Fe3O4 heterogeneous nanosheets was constructed to detect Vitamin K epoxide reductase complex 1 type AA (VKORC1*2). Firstly, we fabricated magnetic α-Fe2O3/Fe3O4 heterogeneous nanosheets as the substrate material for electrochemical biosensors. Subsequently, the material surface was modified with Au nanoparticles to facilitate the connection of sensing probes and enhance current signal amplification. The electrochemical biosensor showed a negative linear relationship with the concentrations of target DNA (tDNA, VKORC1*2 gene) within 1 pM − 1 μM, a limit of detection was (LOD) = 0.36 pM, and a limit of quantification was (LOQ) = 1.19 pM. The biosensor demonstrated exceptional specificity, reproducibility, and stability. In real sample analysis, it exhibited a recovery range of 96.63 % − 110.57 % (RSD ≤ 3.07 %) for various tDNA concentrations, thereby indicating its promising potential in clinical diagnostics.

Abstract Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design. Benefiting from the inherent sparsity and parallelism in massive photonic connections, L2ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field, while incrementally acquiring expertise on various tasks by gradually enlarging the activation. The multi-task optical features are parallelly processed by multi-spectrum representations allocated with different wavelengths. Extensive evaluations on free-space and on-chip architectures confirm that for the first time, L2ONN avoided the catastrophic forgetting issue of photonic computing, owning versatile skills on challenging tens-of-tasks (vision classification, voice recognition, medical diagnosis, etc.) with a single model. Particularly, L2ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks, and 14× larger capacity than existing optical neural networks while maintaining competitive performance on each individual task. The proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme, permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R2 exceeding 0.7) during the training, validation and testing steps. The gap between model R2 values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters.

Abstract Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their potential for parallelization, power efficiency, and speed. Diffractive neural networks, which process information by propagating encoded light through trained optical elements, have garnered interest. However, training large-scale diffractive networks faces challenges due to the computational and memory costs of optical diffraction modeling. Here, we present DANTE, a dual-neuron optical-artificial learning architecture. Optical neurons model the optical diffraction, while artificial neurons approximate the intensive optical-diffraction computations with lightweight functions. DANTE also improves convergence by employing iterative global artificial-learning steps and local optical-learning steps. In simulation experiments, DANTE successfully trains large-scale ONNs with 150 million neurons on ImageNet, previously unattainable, and accelerates training speeds significantly on the CIFAR-10 benchmark compared to single-neuron learning. In physical experiments, we develop a two-layer ONN system based on DANTE, which can effectively extract features to improve the classification of natural images.

Organic-inorganic metal halide perovskites solar cells (PSCs) have been emerging as a counterpart or a supplement of silicon-based solar cells. They have shown various interesting optoelectronic properties and impressive power conversion efficiencies, even outperforming the theoretical limits in tandem configurations. However, challenges such as long-term stability and scalable manufacturing remain significant obstacles to commercialization. Key factors like material composition and crystal quality are essential for the reliability and performance of PSCs. Traditional solution-based processes face challenges in scalability and reproducibility. This has drawn attention to vacuum processes, which have been successfully employed in the commercial mass production of optoelectronic devices like displays. Also, recent innovations in automated deposition systems aided by machine learning offer promising solutions. These technological advancements enable rapid optimization of material combinations and manufacturing processes, facilitating a transition from lab-scale prototypes to industrial applications. This review highlights the converging efforts from multiple disciplines—materials science, process engineering and machine learning—that are essential for the transition from experimental validation to commercial, sustainable energy solutions. In summary, the work sets a path forward, where collective expertize can address lingering challenges, making clean, accessible, and affordable energy an attainable goal.

Wildfires cause great losses to the ecological environment, economy, and people’s safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones.

ABSTRACT: To study the effects of mineral fulvic acid (FuA) on broiler performance, slaughter performance, blood biochemistry index, antioxidant function, immune performance, and intestinal microflora, 360 Arbor Acres (AA) broiler chickens with similar body weights were randomly divided into 5 groups with 6 replicates in each group and 12 chickens in each replicate in the current study. Chickens in the control group (C) were fed with the basal diet, and chickens in the test groups (I, II, III, and IV) were fed with the diet supplemented with 0.05%, 0.1%, 0.2%, and 0.3% mineral FuA, respectively. The indicators were measured on the hatching day, d 21 and d 35. From the whole experimental period, FuA supplement significantly increased average body weight (ABW) (P < 0.05), average daily gain (ADG) of broilers (P < 0.05), and thymus weight (P < 0.05) in II and IV groups, but bascially reduced the pH value of thigh meat. FuA supplement significantly improved aspartate aminotransferase (AST) activity in the group III on d 35 (P < 0.05) and the serum levels of IgA and IgG on d 21 and d 35 (P < 0.05), but reduced glutathione peroxidase (GSH-Px) level on d 21 (P < 0.05) and malondialdehyde (MDA) level in serum on d 35 (P < 0.05). FuA supplement significantly affected the abundance of Barnesiella, Lachnospiraceae, Alistipes, Lactobacillus, and Christensenellaceae on genus level. Differences between group III and other groups were significant in the genera microflora composition on d 21 and d 35. Functional analysis showed that the cecum microbiota were mainly enriched in carbohydrate metabolism, amino acid metabolism, and energy metabolism. In conclusion, FuA may potentially have significant positive effects on the growth performance and immune function of AA chickens through the modulation of the gut microbiota, and the 0.1% FuA was the best in broiler diet based on the present study.

Tourism in urban forests is rapidly becoming an increasing trend; however, rather few studies have used quantitative measurement to describe the relationship between tourism intensity and ecological functions. This study provides a practical framework that integrates ecosystem service value (ESV) assessment, Internet big data mining and spatial regression analysis to identify the spatial response of ESV and land use/land cover change to tourism activities from 2009 to 2019 in the Siming Mountain Region (SMR), a famous tourist resort located in the eastern coastal China. Results showed that between 2009 and 2019 total ESV increased by 7.1%. Nevertheless, there have been drastic transitions in land use types with function adjustments from traditional agricultural production to diversified tourism-oriented services. Significant spatial autocorrelation was identified for the patterns of ESV changes. GWR further highlighted that the relationship between ESV change and rural tourism indicators varied in space. ESV change in the core zone was negatively correlated with changes in catering service spots and recreational venues, whereas it was positively correlated with local lodgings. Ultimately, targeted recommendations and countermeasures for spatial planning and sustainable tourism development of urban forests under new circumstances were discussed.

Water-related ecosystem services (WES) critical to anthropogenic-related water security are being severely degraded, primarily due to climate and land-use change. Clarifying future trends in WES (i.e., water yield and water purification services) will contribute to water resource sustainability. However, most existing studies are single case studies and rarely consider the combined impacts of climate change and land-use change on ecosystem services, resulting in patterns that cannot be adequately summarized. In this study, the “Annual Water Yield” module of the InVEST model was used to estimate water yield and the “Nutrient Delivery Ratio” module was used to analyze water purification services. In order to compare the impacts of different natural conditions and geographic locations on water ecosystem services, 17 typical watersheds throughout China were selected as study areas. Coupled scenarios were also considered when setting climate and land-use scenarios to predict prospective (2020–2100) evolutionary trends in WES. The response of WES under different scenarios was also assessed. Results show that climate change is more likely to affect water yield services than water purification services. The impact of land use on water purification services is more pronounced than its impact on water yield services. Climate change contributed > 90 % to water yields in 15 out of the 17 watersheds, contributed 79.4 % in the Hei River watershed, while only contributing 11.2 % in the Yarkant River watershed. The influence of climate change on total nitrogen (TN) output in eight of the watersheds was greater than that of land-use change, especially in the Min River and Mintuo River watersheds, for which the contribution was > 95 %. Furthermore, water purification services in nine of the watersheds were more affected by land-use change, especially in the Dongting Lake and Hei River watersheds, for which the contribution was > 95 %. The aim of this study is to assist in the planning of sound water resource measures while developing sustainable management strategies to help relevant water resource sectors cope with prospective risks and challenges.

Accurate differentiation of energy consumption information of residential users is of great significance for load planning, scheduling, operation and management of power system, and is the basic premise for realizing intelligent perception of energy system and energy saving and carbon reduction. Considering that the conventional single-layer clustering method has limited clustering stability and clustering effect, this paper takes the key family feature factors as the modified feature quantity of quadratic clustering, and proposes a study of user energy characteristics based on double-layer clustering and modification. Firstly, the user’s energy consumption data is collected and pre-processed, and the user’s energy consumption curve is clustered and analyzed by using the integrated clustering algorithm based on voting and the advantages of each member algorithm. Then, the key family characteristic factors are obtained, and the results of one-layer clustering and key family characteristic factors are combined to carry out two-layer clustering of the same category of users in the form of questionnaire survey. Finally, the nonlinear mapping capability of Support Vector Machine (SVM) is used to reverse correct the results of the one-layer clustering. The actual algorithm data of the residents’ demand response experiment in a southeastern province are compared. The results show that compared with the single-layer clustering algorithm, the proposed method can accurately distinguish the energy consumption characteristics and adjustable potential of different users, and correct the wrong clustering results in the single-layer clustering. The clustering stability and clustering effect have been effectively improved.The example results show that the clustering results modified by SVM can better mine and distinguish user energy characteristics, and can be used to solve the problem of the current demand response clustering algorithm not being able to comprehensively and objectively describe the participation willingness and response-ability of residential users in the implementation process. It can also provide a basis for peak shaving and power grid frequency regulation.

With the rapid advancement of mechanical automation and intelligent processing technology, accurately measuring the surfaces of complex parts has emerged as a significant research challenge. Robotic measurement technology plays a crucial role in facilitating rapid quality inspections during the manufacturing process due to its inherent flexibility. However, the irregular shapes and viewpoint occlusions of complex parts complicate precise measurement. To address these challenges, this work proposes a point cloud registration network for robotic scanning systems and introduces a DBR-Net (Dual-line Registration Network) to overcome the issues of low overlap rates and perspective occlusion that currently impede the registration of certain workpieces. First, feature extraction is performed using a bilinear encoder and multi-level feature interactions of both point-wise and global features. Subsequently, the features are sampled through unanimous voting and fed into the RANSAC (Random Sample Consensus) algorithm for pose estimation, enabling multi-view point cloud registration. Experimental results demonstrate that this method significantly outperforms many existing techniques in terms of feature extraction and registration accuracy, thereby enhancing the overall performance of point cloud registration.

Abstract A monolithic perovskite/silicon tandem solar cell architecture is employed to surpass the single‐junction efficiency limit. Recently, there is an increasing need for the double‐sided textures in the Si bottom cell to be compatible with the solution‐processed perovskite top cell from an industrial perspective. Herein, a silver‐assisted alkaline etching method is applied to fabricate nanoscale Si pyramid textures, and the influence of varying pyramid size (400–900 nm) on the interface morphology and the performance of perovskite/Si tandem cells is investigated. It is demonstrated that electrical shunting starts to increase, and the open‐circuit voltage (VOC) decreases when the texture size exceeds the perovskite thickness (~500nm) due to the non‐uniform top‐cell formation on a rough Si surface. However, when the texture size is reduced to 400–500 nm, all spin‐coated perovskite top‐cell component layers exhibit an even form over the nanopyramid Si, resulting in a high VOC and an enhanced Si bottom cell current (≈1.0 mA cm−2) due to the suppressed reflectance at the top/bottom cell interface without using optical couplers. The double‐sided nanopyramid Si texture offers opportunities to increase tandem cell efficiency while reducing its production cost compared with the commonly used single‐sided textured Si.

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather.

Abstract Epitaxy technology produces high-quality material building blocks that underpin various fields of applications. However, fundamental limitations exist for conventional epitaxy, such as the lattice matching constraints that have greatly narrowed down the choices of available epitaxial material combinations. Recent emerging epitaxy techniques such as remote and van der Waals epitaxy have shown exciting perspectives to overcome these limitations and provide freestanding nanomembranes for massive novel applications. Here, we review the mechanism and fundamentals for van der Waals and remote epitaxy to produce freestanding nanomembranes. Key benefits that are exclusive to these two growth strategies are comprehensively summarized. A number of original applications have also been discussed, highlighting the advantages of these freestanding films-based designs. Finally, we discuss the current limitations with possible solutions and potential future directions towards nanomembranes-based advanced heterogeneous integration. Graphical Abstract