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In an age where computing capabilities are expanding at a breathtaking pace, the advent of Artificial Intelligence-Generated Content (AIGC) technology presents unprecedented opportunities and challenges to the future of design. It is crucial for designers to investigate how to utilize this powerful tool to facilitate innovation effectively. As AIGC technology evolves, it will inevitably shift the expectations of designers, compelling them to delve deeper into the essence of design creativity, transcending traditional sketching or modeling skills. This study provides valuable insights for designers on leveraging AIGC for forward-thinking design innovation. We focus on the representative AIGC tool, “Midjourney”, to explore its integration into design systems for collaborative innovation among content creators. We introduce an AIGC-based Midjourney path for product design and present a supporting tool card set: AMP-Cards. To confirm their utility, we undertook extensive validation through advanced prototype design research, task-specific project practices, and interdisciplinary collaborative seminars. Our findings indicate that AIGC can considerably enhance designers’ efficiency during product development, especially in the “explorative product shape” phase. The technology excels in identifying design styles and quickly producing varied design solutions. Moreover, AIGC’s capacity to swiftly translate creators’ concepts into visual forms greatly aids in multidisciplinary team communication and innovation.

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 10 4 m 2 ) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR ( R 2 val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy ( R 2 val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

Targeting tumour immunosuppressive microenvironment is a crucial strategy in immunotherapy. However, the critical role of the tumour lymph node (LN) immune microenvironment (TLIME) in the tumour immune homoeostasis is often ignored. Here, we present a nanoinducer, NIL-IM-Lip, that remodels the suppressed TLIME via simultaneously mobilizing T and NK cells. The temperature-sensitive NIL-IM-Lip is firstly delivered to tumours, then directed to the LNs following pH-sensitive shedding of NGR motif and MMP2-responsive release of IL-15. IR780 and 1-MT induces immunogenic cell death and suppress regulatory T cells simultaneously during photo-thermal stimulation. We demonstrate that combining NIL-IM-Lip with anti-PD-1 significantly enhances the effectiveness of T and NK cells, leading to greatly suppressed tumour growth in both hot and cold tumour models, with complete response in some instances. Our work thus highlights the critical role of TLIME in immunotherapy and provides proof of principle to combine LN targeting with immune checkpoint blockade in cancer immunotherapy. The tumour lymph node microenvironment is an important contributor to the immune suppressiveness of tumours. Here authors target the tumours and the lymph node simultaneously via a pH and photothermal therapy targeted nanoparticle, and show mobilisation of anti-tumour cytotoxic T cells and NK cells and synergistic therapeutic effect with immune checkpoint blockade.

The ecological environment of arid and semi-arid regions (ASARs) faces significant challenges, highlighting the need for a robust indicator system to assess ecological environmental quality (EEQ) and sustainability. This study investigates Central Asia (CA) using the Google Earth Engine (GEE) to develop a new remote sensing-based ecological index (ASAEI), assessing EEQ from 2000 to 2022 using the CatBoost–SHAP model. The results reveal a distinct spatial pattern in the ASAEI: the southwestern and southeastern regions face more severe ecological challenges, while the northern and central-southern areas exhibit better ecological conditions. The ASAEI exhibits a strong spatial autocorrelation, with high-value clusters in the northern and central-southern regions, where vegetation is dense, and low-value clusters in the southwestern and southeastern desert and Gobi regions. Over time, we observed that ecological degradation shifts from west to east. Overall, ecological restoration in CA exceeds the extent of degradation. Notably, Kazakhstan is primarily experiencing degradation, while other subregions predominantly show signs of restoration. Our analysis indicates that climate conditions and land use types are the primary factors influencing changes in the ASAEI. Furthermore, we project that 54.5% of the CA region will exhibit an improved EEQ, highlighting the need for restoration efforts in the western areas. The ASAEI offers a novel perspective and methodology for assessing EEQ in ASARs, with significant scientific implications.

HighlightsA polarized-macrophages-based drug delivery system (M1/SLNP) was presented for the cell-chemotherapy of cancer.Polarized-macrophages were used both as therapeutic tool to provide immunotherapy and as delivery vessel to target deliver chemotherapeutic drugs to tumor tissues for chemotherapy simultaneously.M1/SLNP was a multifunctional delivery system with simple structure, excellent safety, and without complex synthesis process.Cell therapy is a promising strategy for cancer therapy. However, its therapeutic efficiency remains limited due to the complex and immunosuppressive nature of tumor microenvironments. In this study, the “cell-chemotherapy” strategy was presented to enhance antitumor efficacy. M1-type macrophages, which are therapeutic immune cells with both of immunotherapeutic ability and targeting ability, carried sorafenib (SF)-loaded lipid nanoparticles (M1/SLNPs) were developed. M1-type macrophages were used both as therapeutic tool to provide immunotherapy and as delivery vessel to target deliver SF to tumor tissues for chemotherapy simultaneously. M1-type macrophages were obtained by polarizing macrophages using lipopolysaccharide, and M1/SLNPs were obtained by incubating M1-type macrophages with SLNP. Tumor accumulation of M1/SLNP was increased compared with SLNP (p < 0.01), which proved M1/SLNP could enhance tumor targeting of SF. An increased ratio of M1-type macrophages to M2-type macrophages, and the CD3+CD4+ T cells and CD3+CD8+ T cell quantities in tumor tissues after treatment with M1/SLNP indicated M1/SLNP could relieve the immunosuppressive tumor microenvironments. The tumor volumes in the M1/SLNP group were significantly smaller than those in the SLNP group (p < 0.01), indicating M1/SLNP exhibited enhanced antitumor efficacy. Consequently, M1/SLNP showed great potential as a novel cell-chemotherapeutic strategy combining both cell therapy and targeting chemotherapy.

Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.

With the advent of the era of great intersection and integration, the development of generative artificial intelligence has caused the renewal of design methods, promoting a new paradigm of research in design fundamentals. The study seeks to investigate the research method of design form in the collaborative mode of artificial intelligence, to provide new ideas for design to conduct interdisciplinary research, and to promote design innovation under AI collaboration. This research begins with the design morphology theory, integrates interdisciplinary theories such as bionic design, and topology research, and collaborates with AIGC tools such as Midjourney, Stable Diffusion, and Chilloutmix to conduct case-specific research. To improve the accuracy of the morphological study, parametric design, bi-directional progressive topology optimization, genetic algorithm and simulation analysis, and other methods were also used in the research process to carry out a comprehensive design experiment exploration. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. This study also summarizes the AIGC prompt formula for the industrial design field and proposes an innovative seven-step design form research method with shape finding and shape making. Simultaneously, the pearl shell design morphology research is conducted in collaboration with AI technology, the full case design of the autonomous underwater vehicle is completed, and the efficacy of the seven-step design morphology research method is validated through fluid simulation. AI synergy provides new ideas for complex morphology research, extends and complements design, and plays a crucial role in the phases of morphology exploration, concept generation, and solution implementation, thereby assisting in the exploration of the central content of design morphology.

The Central Asian area is faced with unprecedented challenges due to the fragility of its ecological environment and under the double pressure of global warming and intensified human activities. As a typical transboundary watershed in Central Asia, the ecological system quality of the Ili-Balkhash Lake Basin (IBLB) is of vital importance to the sustainable development of the whole region. In this study, we constructed the Remote Sensing Ecological Index (RSEI) for the IBLB (2000–2020) using the MODIS dataset on the Google Earth Engine platform and applied Sen’s slope and Mann–Kendall (Sen-MK) trend analysis methods to assess RSEI trends. Additionally, geographic detectors and geographically weighted regression models were used to identify and analyze the global and local factors influencing RSEI. The outcome of the study suggests that: (1) The ecological quality of the basin has generally improved, and the proportion of high quality and good areas has risen, but spatial heterogeneity is obvious, and the area around the lake shows a trend of degradation; (2) LST and NDVI were the main natural drivers, and LUI and AWUE were the key human drivers, with significant interactions among the factors; (3) The GWR model further reveals that LST is the main limiting factor of RSEI, and the two are negatively correlated, while there is significant spatial variability in the effects of NDVI, LUI and AWUE on RSEI, with both positive and negative effects. This study lends a scientific basis for revealing the ecological evolution of watersheds in arid zones. The methodological system it constructed can provide a reference for the ecological monitoring and governance practice of transboundary watersheds around the world.

The soil organic matter (SOM) content is a key factor affecting the function and health of soil ecosystems. For measurements of land reclamation and soil fertility, SOM monitoring using visible and near-infrared spectroscopy (Vis-NIR) is one approach to quantifying soil quality, and Vis-NIR is important for monitoring the SOM content in a broad and nondestructive manner. To investigate the influence of environmental factors and Vis-NIR spectroscopy in estimating SOM, 249 soil samples were collected from the Werigan–Kuqa oasis in Xinjiang, China, and their spectral reflectance, SOM content and soil salinity were measured. To classify and improve the prediction accuracy, we also take into account the soil salinity content as a variable indicator. Relevant environmental variables were extracted using remote sensing datasets (land-use/land-cover (LULC), digital elevation model (DEM), World Reference Base for Soil Resources (WRB), and soil texture). On the basis of Savitzky–Golay (S-G) smoothing and first derivative (FD) preprocessing of the original spectrum, three clusters were obtained by K-means clustering through the use of Vis-NIR and used as spectral classification variables. Using Vis-NIR as Model 1, Vis-NIR combined with spectral classification as Model 2, environmental variables as Model 3, and the combination of all the above variables (Vis-NIR, spectral classification, environmental variables, and soil salinity) as Model 4, a SOM content estimation model was constructed using partial least squares regression (PLSR). Using the 249 soil samples, the modeling set contained 166 samples and the validation set contained 83 samples. The results showed that Model 2 (validation r2 = 0.78) was better than Model 1 (validation r2 = 0.76). The prediction accuracy for Model 4 (validation r2 = 0.85) was better than Model 2 (validation r2 = 0.78). Among these, Model 3 was the worst (validation r2 = 0.39). Therefore, the combination of environmental variables with Vis-NIR spectroscopy to estimate SOM content is an important method and has important implications for improving the accuracy of SOM predictions in arid regions.

We herein describe a general approach to 5-trifluoromethyl 1,2,4-triazoles via the [3 + 2]-cycloaddition of nitrile imines generated in situ from hydrazonyl chloride with CF3CN, utilizing 2,2,2-trifluoroacetaldehyde O-(aryl)oxime as the precursor of trifluoroacetonitrile. Various functional groups, including alkyl-substituted hydrazonyl chloride, were tolerated during cycloaddition. Furthermore, the gram-scale synthesis and common downstream transformations proved the potential synthetic relevance of this developed methodology.