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This study aims to address the issues of accuracy and efficiency in sculpture image classification. Due to the diversity and complexity of sculpture images, traditional image processing algorithms perform poorly in capturing the sculptures’ intricate shapes and structural features, resulting in suboptimal classification and recognition performance. To overcome this challenge, this study proposes an innovative image classification method that combines the ResNet50 model from the Deep Convolutional Neural Network (DCNN) with the K-means++ clustering algorithm. ResNet50 is chosen for its powerful feature extraction capabilities and outstanding performance in image classification tasks. At the same time, K-means++   is selected for its optimized initial centroid selection strategy, which enhances the stability and reliability of clustering. After the final convolutional layer of ResNet50, a self-attention module is added. This module learns and generates an attention map, which guides the model on which areas of the image to focus on in subsequent processing. ResNet50 includes residual blocks, each containing multiple convolutional layers and a skip connection, enabling the network to learn differences between inputs and outputs rather than directly learning outputs, thus improving performance. Initially, ResNet50 extracts feature vectors from original images, which are inputted into the K-means + + algorithm for clustering. K-means + + automatically partitions these feature vectors into different categories, achieving unsupervised image classification. The CMU-MINE architectural sculpture dataset is utilized in the experimental section, with ViT-Base, EfficientNet-B4, and ConvNeXt-Tiny as benchmarks to evaluate the proposed ResNet50 + K-means + + image classification approach. The final model achieves a loss value of 0.155 and a recall of 98.9%, significantly outperforming the other three models. In conclusion, performing feature point matching during three-dimensional reconstruction is crucial. This study employs a combined image classification method using the ResNet50 and K-means + + algorithm, optimizing the accuracy issues of traditional classification methods and achieving promising classification results.

Objective To summarize the clinicopathological characteristics and prognostic factors of papillary renal cell carcinoma (pRCC) and to construct clinical and molecular prognostic nomograms using existing databases. Methods Clinical prognostic models were developed using the Surveillance, Epidemiology, and End Results (SEER) database, while molecular prognostic models were constructed using The Cancer Genome Atlas (TCGA) database. Cox regression and LASSO regression were employed to identify clinicopathological features and molecular markers related to prognosis. The accuracy of the prognostic models was assessed using ROC curves, C-index, decision curve analysis (DCA) curves, and calibration plots. Results In the 2004–2015 SEER cohort, Cox regression analysis revealed that age, grade, AJCC stage, N stage, M stage, and surgery were independent predictors of overall survival (OS) and cancer-specific survival (CSS) in pRCC patients. ROC curves, C-index, and DCA curves indicated that the prognostic nomogram based on clinical independent predictors had better predictive ability than TNM staging and SEER staging. Additionally, in the TCGA cohort, M stage, clinical stage, and the molecular markers IDO1 and PLK1 were identified as independent risk factors. The prognostic nomogram based on molecular independent risk factors effectively predicted the 3-year and 5-year OS and CSS for pRCC patients. Conclusions The clinical and molecular nomograms constructed in this study provide robust predictive tools for individualized prognosis in pRCC patients, offering better accuracy than traditional staging systems.

The current study focuses on understanding the factors (subjective norms (SNs), personal attitude, and perceived behaviour control (PBC)) that influence consumer purchase intention regarding organic food from the theory of planned behaviour and health consciousness as an additional factor in Tanzania and Kenya. It further explains the role of knowledge as a moderating variable in organic food purchase intention. A total of 331 responses from Tanzania and 350 responses from Kenya were obtained. Confirmatory factor analysis was applied for validation, and results were analysed using structural equation modeling. SNs, personal attitudes, and health consciousness were found to be significant predictors of organic purchase intention in both countries. Furthermore, findings show that knowledge positively moderates the relationship among SNs, personal attitude, health consciousness, and organic food purchase intention. However, PBC was found to be a weak influencer on consumer purchase intention in Kenya, and no knowledge interaction between PBC and consumer purchase intention in Tanzania was found. The current study theoretically contributes to the literature by introducing the moderating role of knowledge in the relationship. The results show that knowledge interaction increases the effects of the majority of predictors after being introduced in the relationship. Finally, this study provides an understanding of consumers’ perspective regarding their intention to purchase organic foods, which will help stakeholders, such as marketers, retailers, and producers, to achieve marketing strategies for the development of these products.

The site selection of cold-chain logistics parks is an indispensable part of their planning and construction. This study aims to establish site selection model provide a scientific and sustainable for selecting and determining optimal cold chain logistics parks sites. Traditional site selection methods lacking quantitative standards for assessing the reliability of results. In response, this study introduces Bayesian probability theory to construct a Bayesian network model. This model selects and quantifies influencing factors for site selection, establishing a scientifically evaluation indicator system. Subsequently, utilizing K-means clustering analysis to develop a site selection model. The reliability of clustering results is verified using Bayesian discriminant analysis. Furthermore, a city within the first-class cluster is selected to construct a comprehensive suitability evaluation indicator system for cold-chain logistics park location using Geographic Information System (GIS) technology. Jiangsu Province is chosen as the study area to validate the model, and the analysis demonstrates that Suzhou is the most suitable location for establishing a cold-chain logistics park. The comprehensive suitability evaluation further divides Suzhou into five distinct zones, from which the optimal site is identified and confirmed. Overall, the established site selection model provides a scientific and reliable approach for selecting and determining optimal sites.

The ratio of plant transpiration to total terrestrial evapotranspiration (T/ET) captures the role of vegetation in surface–atmosphere interactions. However, its magnitude remains highly uncertain at the global scale. Here we apply an emergent constraint approach that integrates CMIP5 Earth system models (ESMs) with 33 field T/ET measurements to re-estimate the global T/ET value. Our observational constraint strongly increases the original ESM estimates (0.41 ± 0.11) and greatly alleviates intermodel discrepancy, which leads to a new global T/ET estimate of 0.62 ± 0.06. For all the ESMs, the leaf area index is identified as the primary driver of spatial variations of T/ET, but to correct its bias generates a larger T/ET underestimation than the original ESM output. We present evidence that the ESM underestimation of T/ET is, instead, attributable to inaccurate representation of canopy light use, interception loss and root water uptake processes in the ESMs. These processes should be prioritized to reduce model uncertainties in the global hydrological cycle.

Interactions between soil quality and climate change may influence the capacity of croplands to produce sufficient food. Here, we address this issue by using a new dataset of soil, climate and associated yield observations for 12,115 site-years representing 90% of total cereal production in China. Across crops and environmental conditions, we show that high-quality soils reduced the sensitivity of crop yield to climate variability leading to both higher mean crop yield (10.3 ± 6.7%) and higher yield stability (decreasing variability by 15.6 ± 14.4%). High-quality soils improve the outcome for yields under climate change by 1.7% (0.5–4.0%), compared to low-quality soils. Climate-driven yield change could result in reductions of national cereal production of 11.4 Mt annually under representative concentration pathway RCP 8.5 by 2080–2099. While this production reduction was exacerbated by 14% due to soil degradation, it can be reduced by 21% through soil improvement. This study emphasizes the vital role of soil quality in agriculture under climate change.Food demand is increasing, while climate change is impacting the magnitude and stability of crop yields. High-quality soils are able to buffer the negative impacts of climate change and lead to smaller yield reduction and higher yield stability, indicating a potential adaptation strategy.

Purpose The purpose of this paper is to investigate the effect of online service failure on online customer satisfaction and offline customer loyalty, and the moderating role of brand strength is also examined. While extant research on brick and click service mode recognizes the positive spillover effect from offline stores to online stores, this study analyzes the negative spillover effect from online stores to offline stores. Design/methodology/approach This paper tests the hypotheses by two studies. Study 1 is based on a 2 (failure severity: mild vs severe) × 2 (brand strength: strong vs weak) between-subjects experimental design using scenarios in a brick and click retailer context, while study 2 is based on data collected from a scenario-based questionnaire survey and analyzed through the structural equation modeling. Findings The results indicate that participants exposed to severe online service failure show lower online satisfaction as compared to their counterparts exposed to mild online service failure, but they show the similar level of offline loyalty in both degrees of online service failure. Nevertheless, these results are not moderated by brand strength significantly. Research limitations/implications An experimental design and a scenario-based questionnaire survey are used to test the framework. However, the generalizability of the research findings is still limited to a specific study setting. Future research in a different setting is needed to further validate the presented findings. Practical implications The findings suggest that physical service providers should adopt aggressive online expansion strategy to seize the market and pay more attention to online service quality rather than online marketing only. Originality/value This is one of few studies to explore the risk of brick and click service mode, and provide a clear understanding of the likely occurrence of online service failure and its impact on online customer satisfaction and offline customer loyalty. It extends prior research by exploring non-existence of negative perceptual effect from online service failure to offline customer loyalty in the short run and weakening brand effect, which contributes to cross-channel spillover effect in the integrated multi-channel context and brand building in the internet era.

Increased human water use combined with climate change have aggravated water scarcity from the regional to global scales. However, the lack of spatially detailed datasets limits our understanding of the historical water use trend and its key drivers. Here, we present a survey-based reconstruction of China’s sectoral water use in 341 prefectures during 1965 to 2013. The data indicate that water use has doubled during the entire study period, yet with a widespread slowdown of the growth rates from 10.66 km³·y−2 before 1975 to 6.23 km³·y−2 in 1975 to 1992, and further down to 3.59 km³·y−2 afterward. These decelerations were attributed to reduced water use intensities of irrigation and industry, which partly offset the increase driven by pronounced socioeconomic development (i.e., economic growth, population growth, and structural transitions) by 55% in 1975 to 1992 and 83% after 1992. Adoptions for highly efficient irrigation and industrial water recycling technologies explained most of the observed reduction of water use intensities across China. These findings challenge conventional views about an acceleration in water use in China and highlight the opposing roles of different drivers for water use projections.

A dual-band broadband absorber based on frequency selective surface (FSS) is proposed. This double-layer absorber consists of two layers of FSS structure; each layer has a unique construction and plays its wave-absorbing function in different frequency bands. In the low-frequency band, layer I is a transmission channel, and layer II effectively absorbs electromagnetic waves. Furthermore, layer I exhibits significant absorption characteristics in the high-frequency band, whereas layer II provides effective reflection. The combination of the two layers results in a remarkably effective absorption. Its reflectivity below − 10 dB covers a bandwidth of 5.5–14.8 GHz and 22.9–33.1 GHz, and its fractional bandwidth is 128%. The structure prevents external electromagnetic interference from affecting the antenna’s performance, ensuring communication quality in critical frequency bands while reducing unwanted absorption in unwanted frequency bands. It is worth mentioning that the structure still has good stability under 45° oblique incidence. The physical mechanism is analyzed in detail by using an equivalent circuit model (ECM). The measurement results are in good agreement with those of simulation and ECM.

Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.