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Abstract Immunotherapy has made significant strides in cancer treatment, particularly through immune checkpoint blockade (ICB), which has shown notable clinical benefits across various tumor types. Despite the transformative impact of ICB treatment in cancer therapy, only a minority of patients exhibit a positive response to it. In patients with solid tumors, those who respond well to ICB treatment typically demonstrate an active immune profile referred to as the “hot” (immune-inflamed) phenotype. On the other hand, non-responsive patients may exhibit a distinct “cold” (immune-desert) phenotype, differing from the features of “hot” tumors. Additionally, there is a more nuanced “excluded” immune phenotype, positioned between the “cold” and “hot” categories, known as the immune “excluded” type. Effective differentiation between “cold” and “hot” tumors, and understanding tumor intrinsic factors, immune characteristics, TME, and external factors are critical for predicting tumor response and treatment results. It is widely accepted that ICB therapy exerts a more profound effect on “hot” tumors, with limited efficacy against “cold” or “altered” tumors, necessitating combinations with other therapeutic modalities to enhance immune cell infiltration into tumor tissue and convert “cold” or “altered” tumors into “hot” ones. Therefore, aligning with the traits of “cold” and “hot” tumors, this review systematically delineates the respective immune characteristics, influencing factors, and extensively discusses varied treatment approaches and drug targets based on “cold” and “hot” tumors to assess clinical efficacy.

In order to investigate the combustion phenomenon and reaction kinetics parameters of Li/SF6 under different temperature and pressure conditions, the Li/SF6 combustion test platform with shock wave-induced high pressure thermal load excitation was used to measure the ignition delay period at the temperature of 830~1 400 K and the pressure range of 0.8~11 atm. The laws of Li/SF6 combustion process and basic luminescence phenomena were investigated by the visual experiment section. Based on the typical Arrhenius dependence between ignition delay period and temperature and pressure, the reaction kinetics parameters were obtained by multiple linear regression method. The results show that the ignition delay period of Li/SF6 combustion decreases with the increase in temperature and pressure, and the luminescence phenomenon gradually changes from a red isolated fire nucleus to a white bright flame with the increase in pressure. The pre-index factor A, exponential factor N, and activation energy Ea are obtained based on the experimentally measured ignition delay period, which provides an important basis for the identification of combustion characteristics and the construction of numerical simulation.

Abstract In-sensor processing has the potential to reduce the energy consumption and hardware complexity of motion detection and recognition. However, the state-of-the-art all-in-one array integration technologies with simultaneous broadband spectrum image capture (sensory), image memory (storage) and image processing (computation) functions are still insufficient. Here, macroscale (2 × 2 mm2) integration of a rippled-assisted optoelectronic array (18 × 18 pixels) for all-day motion detection and recognition. The rippled-assisted optoelectronic array exhibits remarkable uniformity in the memory window, optically stimulated non-volatile positive and negative photoconductance. Importantly, the array achieves an extensive optical storage dynamic range exceeding 106, and exceptionally high room-temperature mobility up to 406.7 cm2 V−1 s−1, four times higher than the International Roadmap for Device and Systems 2028 target. Additionally, the spectral range of each rippled-assisted optoelectronic processor covers visible to near-infrared (405 nm–940 nm), achieving function of motion detection and recognition.

The cost of hyperspectral image (HSI) classification primarily stems from the annotation of image pixels. In real-world classification scenarios, the measurement and annotation process is both time-consuming and labor-intensive. Therefore, reducing the number of labeled pixels while maintaining classification accuracy is a key research focus in HSI classification. This paper introduces a multi-strategy triple network classifier (MSTNC) to address the issue of limited labeled data in HSI classification by improving learning strategies. First, we use the contrast learning strategy to design a lightweight triple network classifier (TNC) with low sample dependence. Due to the construction of triple sample pairs, the number of labeled samples can be increased, which is beneficial for extracting intra-class and inter-class features of pixels. Second, an active learning strategy is used to label the most valuable pixels, improving the quality of the labeled data. To address the difficulty of sampling effectively under extremely limited labeling budgets, we propose a new feature-mixed active learning (FMAL) method to query valuable samples. Fine-tuning is then used to help the MSTNC learn a more comprehensive feature distribution, reducing the model’s dependence on accuracy when querying samples. Therefore, the sample quality is improved. Finally, we propose an innovative dual-threshold pseudo-active learning (DSPAL) strategy, filtering out pseudo-label samples with both high confidence and uncertainty. Extending the training set without increasing the labeling cost further improves the classification accuracy of the model. Extensive experiments are conducted on three benchmark HSI datasets. Across various labeling ratios, the MSTNC outperforms several state-of-the-art methods. In particular, under extreme small-sample conditions (five samples per class), the overall accuracy reaches 82.97% (IP), 87.94% (PU), and 86.57% (WHU).

IntroductionThis study addresses the urgent need for non-destructive identification of commercially valuable Dalbergia species, which are threatened by illegal logging. Effective identification methods are crucial for ecological conservation, biodiversity preservation, and the regulation of the timber trade.MethodsWe integrate Visible/Near-Infrared (Vis/NIR) Hyperspectral Imaging (HSI) with advanced machine learning techniques to enhance the precision and efficiency of wood species identification. Our methodology employs various modeling approaches, including Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN). These models analyze spectral data across Vis (383–982 nm), NIR (982–2386 nm), and full spectral ranges (383 nm to 2386 nm). We also assess the impact of preprocessing techniques such as Standard Normal Variate (SNV), Savitzky-Golay (SG) smoothing, normalization, and Multiplicative Scatter Correction (MSC) on model performance.ResultsWith optimal preprocessing, both SVM and CNN models achieve 100% accuracy across NIR and full spectral ranges. The selection of an appropriate wavelength range is critical; utilizing the full spectrum captures a broader array of the wood's chemical and physical properties, significantly enhancing model accuracy and predictive power.DiscussionThese findings underscore the effectiveness of Vis/NIR HSI in wood species identification. They also highlight the importance of precise wavelength selection and preprocessing techniques to maximize both accuracy and cost-efficiency. This research contributes substantially to ecological conservation and the regulation of the timber trade by providing a reliable, non-destructive method for identifying threatened wood species.

Abstract Phoebe zhennan and Phoebe bournei are favored for their fine-grain, insect resistance, special fragrance, not easily cracked and deformed, which had been widely used in furniture and construction. Wood authenticity regarding different varieties and geographical origins is increasingly becoming a concern for consumers. Due to the difference in wood quality characteristics and economic value of genus Phoebe species from different geographical origins, it is important to establish a fast, efficient, and reliable method to discriminate the geographical origin of Phoebe zhennan and Phoebe bournei. Solid phase micro-extraction (SPME) was used as sample preparation approach and gas chromatography-mass spectrometry (GC–MS) technique was employed to analyze the volatile compounds. As a result, a total of 40 volatiles were identified by SPME–GC–MS among Phoebe zhennan samples from four origins, while 34 substances identified among Phoebe bournei samples. The wood samples of Phoebe zhennan and Phoebe bournei from different major production areas in China were able be discriminated by GC–MS spectroscopy. This finding indicated that genus Phoebe species from different climate zones had different chemical composition, which verified that the SPME–GC–MS technique can be used to classify different species of wood with characteristic compounds.

Abstract Neuromorphic computing can efficiently handle data‐intensive tasks and address the redundant interaction required by von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. 2D phosphorene, such as violet phosphorene, show great potential in optoelectronics due to their strong light‐matter interactions, while current research is mainly focused on synthesis and characterization, its application in photoelectric devices is vacant. Here, the authors combined violet phosphorene and molybdenum disulfide to demonstrate an optoelectronic synapse with a light‐to‐dark ratio of 106, benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including a dynamic range (DR) of > 60 dB, 128 (7‐bit) distinguishable conductance states, electro‐optical dependent plasticity, short‐term paired‐pulse facilitation, and long‐term potentiation/depression. Thanks to the excellent DR and multi‐states, high‐precision image classification with accuracies of 95.23% and 79.65% is achieved for the MNIST and complex Fashion‐MNIST datasets, which is close to the ideal device (95.47%, 79.95%). This work opens the way for the use of emerging phosphorene in optoelectronics and provides a new strategy for building synaptic devices for high‐precision neuromorphic computing.

Abstract Near infrared hyperspectral imaging (NIR-HSI) spectroscopy can be a rapid, precise, low-cost and non-destructive way for wood identification. In this study, samples of five Guiboutia species were analyzed by means of NIR-HSI. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used after different data treatment in order to improve the performance of models. Transverse, radial, and tangential section were analyzed separately to select the best sample section for wood identification. The results obtained demonstrated that NIR-HSI combined with successive projections algorithm (SPA) and SVM can achieve high prediction accuracy and low computing cost. Pre-processing methods of SNV and Normalize can increase the prediction accuracy slightly, however, high modelling accuracy can still be achieved by raw pre-processing. Both models for the classification of G. conjugate , G. ehie and G. demeusei perform nearly 100% accuracy. Prediction for G. coleosperma and G. tessmannii were more difficult when using PLS-DA model. It is evidently clear from the findings that the transverse section of wood is more suitable for wood identification. NIR-HSI spectroscopy technique has great potential for Guiboutia species analysis.

Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted capacity to acquire profound characteristics, posing a persistent challenge in reliably and effectively detecting anomalies in power consumption data. Hence, the present study puts forth a hybrid model that amalgamates a convolutional neural network (CNN) and a transformer network as a means to tackle this concern. The CNN model with a dual-scale dual-branch (DSDB) structure incorporates inter- and intra-periodic convolutional blocks to conduct shallow feature extraction of sequences from varying dimensions. This enables the model to capture multi-scale features in a local-to-global fashion. The transformer module with Gaussian weighting (GWT) effectively captures the overall temporal dependencies present in the electricity consumption data, enabling the extraction of sequence features at a deep level. Numerous studies have demonstrated that the proposed method exhibits enhanced efficiency in feature extraction, yielding high F1 scores and AUC values, while also exhibiting notable robustness.

Water bodies in urban green spaces are important parts of urban landscapes, and their planar shapes are an important factor governing the creation of waterfront landscapes. To improve the aesthetics and functionality of water bodies, this paper takes Nanjing as an example for analysis to investigate whether it is possible to scientifically quantify the planar shape of urban green space water bodies. First, water bodies meeting the conditions within the municipal area were selected as the study objects for classification. Second, in view of the lack of theoretical and innovative problems in previous studies, the use of fractal theory was proposed to improve the scientificity. Finally, remote sensing data images were used to extract water body planes, and the fractal dimensions were calculated and quantitatively evaluated by coupling the box dimension method with fractal theory. The results show that the fractal dimension could be used as a quantitative parameter to determine the planar morphology of water bodies in urban green spaces, and the fractal dimension value is positively correlated with the complexity of the water body, which can be used for both quantitative assessment of the landscape aesthetics of existing water bodies in urban green spaces and theoretical support for the future design of water planar morphology.

Tourism can bring economic development and social benefits to cities. At present, global tourism is the leading urban tourism development model in China, and there is a growing tendency to use global tourism demonstration cities as models for urban tourism development; however, existing research has mostly focused on the theoretical level, and it is unclear whether such cities achieve sustainable development on a realistic level. This study selected the first demonstration cities of global tourism in China and conducted a coupling analysis using multi-source big data, clustering algorithm models, regional tourism flow distribution characteristics, etc., to explore whether the model cities meet development requirements. The following findings can be drawn from the analysis results. Firstly, the clustering algorithm coupled model study can provide a more accurate assessment of the current situation of regional tourism compared to the thermal values; secondly, the selected cities did not meet the development requirements of sustainable tourism and are in urgent need of improvement. The overarching contribution of this study is to propose a quantitative and replicable framework for urban tourism evaluation, combining spatial big data, computer algorithmic models and urban economics, etc.; this study also extends the interpretation of global tourism cities, reminds scholars, urban planners and urban tourism managers not to underestimate the possible tourism-related unsustainability of global tourism cities, and provides theoretical support for future tourism construction and urban planning development in China.

The purposes are to analyze the mechanism of digitized landscape architecture design and stablize the garden landscape image display in constructing garden landscape digitization platform. According to previous research and mobile edge computing, a scheme of digitized landscape architecture design is proposed based on edge computing. This scheme uses discrete elevation calculation to preserve the landscape design image's frame. It adopts the Roberts edge detection and Laplacian operator for high-level stable preservation of landscape images. Simultaneously, the displayed image is stablized using edge computing algorithms. Simulation experiments are performed to verify the effectiveness of the proposed scheme of digitized landscape architecture design based on mobile edge computing. Results demonstrate that the discrete elevation calculation algorithm can avoid low visual rendering in the 3D image generation process, optimize the seed point matching of edge correlation, and ensure image clarity and stability. The edge computing algorithm can fundamentally avoid the problem of image shaking. The impact of different algorithm models on the classification and accuracy of landscape images is analyzed through parameter optimization. Compared with some latest models, the proposed landscape design scheme based on edge computing has better accuracy. The average accuracy can reach more than 90%, and the Kappa coefficient remains at 86.93%. The designed garden landscape digitization platform can stably display 3D garden landscape images while avoiding the shaking of 3D images, which can provide a theoretical basis and practical value for designing and planning landscape architecture.

Colloidal quantum dots, with their size-tunable optoelectronic properties and scalable synthesis, enable applications in which inexpensive high-performance semiconductors are needed. Synthesis science breakthroughs have been key to the realization of quantum dot technologies, but important group III-group V semiconductors, including colloidal gallium arsenide (GaAs), still cannot be synthesized with existing approaches. The high-temperature molten salt colloidal synthesis introduced in this work enables the preparation of previously intractable colloidal materials. We directly nucleated and grew colloidal quantum dots in molten inorganic salts by harnessing molten salt redox chemistry and using surfactant additives for nanocrystal shape control. Synthesis temperatures above 425°C are critical for realizing photoluminescent GaAs quantum dots, which emphasizes the importance of high temperatures enabled by molten salt solvents. We generalize the methodology and demonstrate nearly a dozen III-V solid-solution nanocrystal compositions that have not been previously reported. [ABSTRACT FROM AUTHOR]