Automatic Retrieval Research Articles

Receiving Non-Contact Information About the Object through a Mobile Application

The article introduces a mobile application that enables users to retrieve information about objects by simply approaching them with their phone. This eliminates the need for manual searching and allows for automatic retrieval of relevant information on the phone. This non-contact method of retrieving information makes it easier to preserve and utilize objects, saving time and resources. The mobile application that has been developed comprises of various modules such as object identification, object data collection and processing, database modeling, and information retrieval. It has been implemented using modern technologies and equipment using Java language. This application can be used effectively in multiple fields such as museums, parks, medicine, education, among others. In the field of education, this app provides additional resources and a more interactive learning experience that goes beyond traditional textbooks or presentations.

Barriers against Implementation of European Society of Gastrointestinal Endoscopy Performance Measures for Colonoscopy in Clinical Practice.

Background and Objectives: The implementation and monitoring of the European Society of Gastrointestinal Endoscopy (ESGE) performance measures for colonoscopy are suboptimal in clinical practice. Electronic reporting systems may play an important role in data retrieval. We aimed to define the possibility of systematically assessing and monitoring ESGE performance measures for colonoscopy through reporting systems. Materials and Methods: We conducted a survey during a nationwide event on the quality of colonoscopy held in Rome, Italy, in March 2023 by a self-administered questionnaire. Analyses were conducted overall and by workplace setting. Results: The attendance was 93% (M/F 67/26), with equal distribution of age groups, regions and public or private practices. Only about one-third (34%) and 21.5% of participants stated that their reporting system allows them to retrieve all the ESGE performance measures, overall and as automatic retrieval, respectively. Only 66.7% and 10.7% of respondents can systematically report the cecal intubation and the adenoma detection rate, respectively. The analysis according to hospital setting revealed no significant difference for all the items. Conclusions: We found a generalized lack of systematic tracking of performance measures for colonoscopy due to underperforming reporting systems. Our results underline the need to update reporting systems to monitor the quality of endoscopy practice in Italy.

Functional integration and segregation during semantic cognition: Evidence across age groups

Semantic cognition is underpinned by ventral anterior temporal lobe (vATL) which encodes knowledge representations and inferior frontal gyrus (IFG), which controls activation of knowledge based on the needs of the current context. This core semantic network has been validated in substantial empirical findings in the past. However, it remains unclear how these core semantic areas dynamically communicate with each other, and with other neural networks, to achieve successful semantic processing. Here, we investigated this question by testing functional connectivity in the core semantic network during semantic tasks and whether these connections were affected by cognitive ageing. Compared to a non-semantic task, semantic tasks increased the connectivity between left and right IFGs, indicating a bilateral semantic control system. Strengthened connectivity was also found between left IFG and left vATL, and this effect was stronger in the young group. At a whole-brain scale, IFG and vATL increased their coupling with multiple-demand regions during semantic tasks, even though these areas were deactivated relative to non-semantic tasks. This suggests that the domain-general executive network contributes to semantic processing. In contrast, IFG and vATL decreased their interaction with default mode network (DMN) areas during semantic tasks, even though these areas were positively activated by the task. This suggests that DMN areas do not contribute to all semantic tasks: their activation may sometimes reflect automatic retrieval of task-irrelevant memories and associations. Taken together, our study characterizes a dynamic connectivity mechanism supporting semantic cognition within and beyond core semantic regions.

Lessons learned from an electronic botany dictionary compilation project

The study offers insights into the design, working model, and solutions of the specialised electronic dictionary of botany terms in Latvian, Latin, English, German, and Russian, developed as a mobile application. The third version of this dictionary has been updated as a final one within the project ‘Smart Complex of Information Systems of Specialised Biology Lexis for the Research and Preservation of Linguistic Diversity’ and published in the Google Play Store and the Apple Store in January 2024.The study describes various aspects of the conception of a dictionary of botany terms and offers a practical dictionary compiling model for the dictionary’s mega-, macro-, and microstructure. Prior to commencing the project, a survey of the potential users was conducted to understand the preferences of the respondents and to take these preferences into consideration when developing the dictionary. New solutions to the aspects typical of a specialised dictionary of botany terms (labelling of taxonomic categories, visualization with interactive images, automatic retrieval of definitions from existing sources and their usability, etc.) were found. The study is summarised by discussing the main lessons that have been learned in the process of developing a small(~3000 entry words) specialised dictionary. The conclusions might be useful for lexicographers and translators interested in compiling dictionaries.

Artificial Intelligence Retrieval System Based on Computer Big Data Technology

In order to achieve automatic retrieval of library literature, the author proposes a robot retrieval method based on intention recognition for library literature automatic retrieval. Firstly, propose a robot retrieval approach; Then, the keywords of the user’s question are automatically extracted, and the intention of the user’s question is identified. Finally, combined with intention recognition, the semantic similarity between the intention recognition literature and the search literature is calculated. The latest results indicate that: The accuracy of keyword automatic extraction based on KL and TF-IDF for each sentence keyword is 98.3%, indicating that this extraction method performs better. The retrieval accuracy of the automatic retrieval robot reaches 98%, indicating that this method can accurately achieve accurate retrieval of user intentions and candidate literature.

Research on and Application of Feature Recognition and Intelligent Retrieval Method for Multi-Component Alloy Powder Injection Molding Gear Based on Partition Templates

The forming process of multi-alloy gears by metal powder injection molding is tedious, and the current design process mainly depends on the experience of designers, which seriously affects the product development cycle and forming quality. In order to solve the problem of the gear feature expression being missing, which hinders the automatic retrieval of similar parts in the analogical design process, a feature recognition and intelligent retrieval method for a multi-alloy powder injection molding gear based on partition templates is proposed in this paper. The partition templates of the gear are defined, and gear digitization is completed by using the automatic recognition algorithm. Searching for similar gear parts in the knowledge base, designers can analogically design the forming process for new parts according to the mature process of the parts in the knowledge base. The automatic identification and intelligent retrieval system developed according to this method has been implemented in two MIM (metal injection molding) product manufacturing enterprises. Case studies and industrial applications have proved the effectiveness of the system, the efficiency of identification and retrieval has been improved by more than 97%, and the number of mold tests has been reduced by 60%.

Productive performance of cassava (Manihot esculenta Crantz) in the Maranhense Mesorregions of the Amazon Biome

Cassava (Manihot esculenta Crantz) is a cultivar that is widely distributed in the mesoregions of the state of Maranhão in Brazil, and is highly prized for its value as a food crop, economic sphere and social and cultural contribution. In addition, the species is adaptable to different biomes, culminating in its agricultural expansion throughout the country. In this context, the aim of this study is to highlight the socio-economic importance of cassava in the mesoregions of Maranhão that cover the Amazon Biome, since both agriculture and livestock farming have an impact on the use and preservation of this area. To this end, a statistical analysis was carried out using the IBGE Automatic Retrieval System (SIDRA) for the 2015-2022 inter-annual period for the mesoregions of North, West and Central Maranhão, including the Biome under study, in terms of productivity. As a result, it was possible to see a sharp drop in cassava production in the Northeast, from 816.259 ha-1 (2010) to 390.472 ha-1 (2022) of planted area, a reduction of 52,17%. In addition, the pattern of decline followed for harvested area, quantity produced and average yield. In summary, despite the respective importance of cassava, the crop faces major challenges as it competes with other economically valuable crops for arable land, as well as competition from strong cattle farming. The fact is that just producing cassava is not enough to sustain it. It is necessary to master the practices and techniques that optimize the cassava agricultural production system.

What drives the automatic retrieval of real-world object size knowledge?

Real-world object size is a behaviorally relevant object property that is automatically retrieved when viewing object images: participants are faster to indicate the bigger of two object images when this object is also bigger in the real world. What drives this size Stroop effect? One possibility is that it reflects the automatic retrieval of real-world size after objects are recognized at the basic level (e.g., recognizing an object as a plane activates large real-world size). An alternative possibility is that the size Stroop effect is driven by automatic associations between low-/mid-level visual features (e.g., rectilinearity) and real-world size, bypassing object recognition. Here, we tested both accounts. In Experiment 1, objects were displayed upright and inverted, slowing down recognition while equating visual features. Inversion strongly reduced the Stroop effect, indicating that object recognition contributed to the Stroop effect. Independently of inversion, however, trial-wise differences in rectilinearity also contributed to the Stroop effect. In Experiment 2, the Stroop effect was compared between manmade objects (for which rectilinearity was associated with size) and animals (no association between rectilinearity and size). The Stroop effect was larger for animals than for manmade objects, indicating that rectilinear feature differences were not necessary for the Stroop effect. Finally, in Experiment 3, unrecognizable "texform" objects that maintained size-related visual feature differences were displayed upright and inverted. Results revealed a small Stroop effect for both upright and inverted conditions. Altogether, these results indicate that the size Stroop effect partly follows object recognition with an additional contribution from visual feature associations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Quality control in the establishment and management of gastric cancer database

The establishment of a high-quality gastric cancer database significantly improves the efficiency and standardization of diagnosis and treatment of this disease. Our center has developed a specialized, single-center gastric cancer database and initiated the China Gastrointestinal Cancer Surgery Union, catalyzing the exploration of multi-center databases. This article encapsulates multi-level experience and provides a detailed overview of the quality control methods we implement in both constructing and managing the gastric cancer database. Utilizing an electronic medical record system and a multi-disciplinary treatment (MDT) approach, we have designed the database in a modular and multi-nodal manner. A synthesis of automatic retrieval of structured data and manual entry, coupled with a rigorous MDT system and real-time supervision at various nodes, bolster our real-time quality control efforts. Ensuring data security and digitized management plans alongside real-time review protocol and a multi-level review system, we maintain the highest standards in the initiation and management of the database. Through the establishment of the China Gastrointestinal Cancer Surgery Union platform, we endorse the concept that multi-center database construction should be driven by research objectives, consider data accessibility, while placing an emphasis on building inter-center consensus on data quality control. Moving forward, it is crucial that the development of multi-center databases promotes uniformity in medical standards across centers, cultivates stable public data sharing platforms, ensures robust data security protocols, routinely conducts data quality assessments, and bolsters multi-center cooperation and exchanges to promote the homogeneity of medical standards.

ARCTIC-3D: automatic retrieval and clustering of interfaces in complexes from 3D structural information

The formation of a stable complex between proteins lies at the core of a wide variety of biological processes and has been the focus of countless experiments. The huge amount of information contained in the protein structural interactome in the Protein Data Bank can now be used to characterise and classify the existing biological interfaces. We here introduce ARCTIC-3D, a fast and user-friendly data mining and clustering software to retrieve data and rationalise the interface information associated with the protein input data. We demonstrate its use by various examples ranging from showing the increased interaction complexity of eukaryotic proteins, 20% of which on average have more than 3 different interfaces compared to only 10% for prokaryotes, to associating different functions to different interfaces. In the context of modelling biomolecular assemblies, we introduce the concept of “recognition entropy”, related to the number of possible interfaces of the components of a protein-protein complex, which we demonstrate to correlate with the modelling difficulty in classical docking approaches. The identified interface clusters can also be used to generate various combinations of interface-specific restraints for integrative modelling. The ARCTIC-3D software is freely available at github.com/haddocking/arctic3d and can be accessed as a web-service at wenmr.science.uu.nl/arctic3d.

Aerosol Optical Properties Retrieved by Polarization Raman Lidar: Methodology and Strategy of a Quality-Assurance Tool

Aerosol optical properties retrieved using polarization Raman lidar observations play an increasingly vital role in meteorology and environmental protection. The quality of the data products directly affects the impact of relevant scientific applications. However, the quality of aerosol optical properties retrieved from polarization Raman lidar signals is difficult to assess. Various factors, such as hardware system performance, retrieval algorithm, and meteorological conditions at the observation site, influence data quality. In this study, we propose a method that allows for assessing the reliability of aerosol optical properties derived from polarization Raman lidar observations. We analyze the factors that affect the reliability of retrieved aerosol optical properties. We use scoring methods combined with a weight-assignment scheme to evaluate the quality of the retrieved aerosol optical properties. The scores and weights of each factor are arranged based on our analysis of a simulation study and the characteristics of each factor. We developed an automatic retrieval algorithm that allows for deriving homogeneous aerosol optical data sets. We also assess with this method the quality of retrieved aerosol optical properties obtained with different polarization Raman lidars under different measurement scenarios. Our results show that the proposed quality assurance method can distinguish the reliability of the retrieved aerosol optical properties.

MFSCNN: APPENDING A MASKED BRANCH TO FAST-SCNN TO IMPROVE ROAD MARKING EXTRACTION ON SPARSE MLS POINT CLOUD-DERIVED IMAGES

Abstract. With the rise of self-driving cars, an increasing number of vehicles are equipped with low-cost light detection and ranging (LiDAR) sensors that could potentially serve as a massive mobile mapping resource, particularly for jobs that require multiple and frequent scanning, such as maintaining dynamic high-definition maps or digital twins. However, low-cost LiDAR sensors produce sparser point clouds during scanning which can make deep learning techniques for the automatic retrieval of features difficult like extracting road markings. In this work, we aim to improve the performance of a convolutional neural network (CNN) model for road marking extraction from sparse mobile LiDAR scanning (MLS) point cloud-derived images. We propose the modification of the Fast-SCNN model structure by adding a 2D convolution branch with masking in the feature fusion step: MFSCNN. To retain speed we only use MFSCNN to boost model training and still utilize Fast-SCNN for inference. Our results indicate potential, with a 4.6% increase in mean f1-score and an 8% decrease in uncertainty for the road marking class after multiple trials. Additionally, this research aims to support and increase research interest in lower-cost LiDARs for mobile mapping.

Testing the automaticity features of the affect misattribution procedure: The roles of awareness and intentionality.

The affect misattribution procedure (AMP) is a measure of implicit evaluations, designed to index the automatic retrieval of evaluative knowledge. The AMP effect consists in participants evaluating neutral target stimuli positively when preceded by positive primes and negatively when preceded by negative primes. After multiple prior tests of intentionality, Hughes et al. (Behav Res Methods 55(4):1558-1586, 2023) examined the role of awareness in the AMP and found that AMP effects were larger when participants indicated that their response was influenced by the prime than when they did not. Here we report seven experiments (six preregistered; N = 2350) in which we vary the methodological features of the AMP to better understand this awareness effect. In Experiments 1-4, we establish variability in the magnitude of the awareness effect in response to variations in the AMPprocedure. By introducing further modifications to the AMPprocedure, Experiments 5-7 suggest an alternative explanation of the awareness effect, namely that awareness can be the outcome, rather than the cause, of evaluative congruency between primes and responses: Awareness effects emerged even when awareness could not have contributed to AMP effects, including when participants judged influence awareness for third parties or primes were presented post hoc. Finally, increasing the evaluative strength of the primes increased participants' tendency to misattribute AMP effects to the influence of target stimuli. Together, the present findings suggest that AMP effects can create awareness effects rather than vice versa and support the AMP's construct validity as a measure of unintentional evaluations of which participants are also potentially unaware.

Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach

Detecting lung pathologies is critical for precise medical diagnosis. In the realm of diagnostic methods, various approaches, including imaging tests, physical examinations, and laboratory tests, contribute to this process. Of particular note, imaging techniques like X-rays, CT scans, and MRI scans play a pivotal role in identifying lung pathologies with their non-invasive insights. Deep learning, a subset of artificial intelligence, holds significant promise in revolutionizing the detection and diagnosis of lung pathologies. By leveraging expansive datasets, deep learning algorithms autonomously discern intricate patterns and features within medical images, such as chest X-rays and CT scans. These algorithms exhibit an exceptional capacity to recognize subtle markers indicative of lung diseases. Yet, while their potential is evident, inherent limitations persist. The demand for abundant labeled data during training and the susceptibility to data biases challenge their accuracy. To address these formidable challenges, this research introduces a tailored computer-assisted system designed for the automatic retrieval of annotated medical images that share similar content. At its core lies an intelligent deep learning-based features extractor, adept at simplifying the retrieval of analogous images from an extensive chest radiograph database. The crux of our innovation rests upon the fusion of YOLOv5 and EfficientNet within the features extractor module. This strategic fusion synergizes YOLOv5's rapid and efficient object detection capabilities with EfficientNet's proficiency in combating noisy predictions. The result is a distinctive amalgamation that redefines the efficiency and accuracy of features extraction. Through rigorous experimentation conducted on an extensive and diverse dataset, our proposed solution decisively surpasses conventional methodologies. The model's achievement of a mean average precision of 0.488 with a threshold of 0.9 stands as a testament to its effectiveness, overshadowing the results of YOLOv5 + ResNet and EfficientDet, which achieved 0.234 and 0.257 respectively. Furthermore, our model demonstrates a marked precision improvement, attaining a value of 0.864 across all pathologies—a noteworthy leap of approximately 0.352 compared to YOLOv5 + ResNet and EfficientDet. This research presents a significant stride toward enhancing radiologists' workflow efficiency, offering a refined and proficient tool for retrieving analogous annotated medical images.

Simple questions for complex matters?

Renewable energy sources have emerged as a current subject matter in Sweden amidst discussions regarding energy costs, climate change and development of energy production. This study explores how Google Search is used for seeking information about wind power and how utilised search queries contribute to the understanding of this energy source. Adopting a practice theoretical perspective, the study explores search queries as doings and sayings, and understands search engines as an established part of everyday routinised information seeking-activities. Data collection was carried out in a trace ethnographic vein through the automatic retrieval of search queries enacted between November 2021 and October 2022. Through a digital methods approach, the search queries were analysed and visualised according to their prevalence and character string composition. A qualitative, multiple coding approach was moreover used for the identification and interpretation of themes and subthemes. The results show that geographical locations, wind power functions and small wind turbines comprise the most prominent subthemes of the search queries. This is replicated also in the search term frequencies, providing further insights to queries related to wind turbine’s efficiency as well as subthemes of advantages and disadvantages. Moreover, the study shows the tendency to phrase search queries as simple questions for complex matters, with nuances being lost in the pursuit of austere, uncomplicated answers. Altogether, the results contribute to a wider understanding of how environmental information seeking is conducted today.

Effective Digital Technology Enabling Automatic Recognition of Special-Type Marking of Expiry Dates

In this study, we present a machine-learning-based approach that focuses on the automatic retrieval of engraved expiry dates. We leverage generative adversarial networks by augmenting the dataset to enhance the classifier performance and propose a suitable convolutional neural network (CNN) model for this dataset referred to herein as the CNN for engraved digit (CNN-ED) model. Our evaluation encompasses a diverse range of supervised classifiers, including classic and deep learning models. Our proposed CNN-ED model remarkably achieves an exceptional accuracy, reaching a 99.88% peak with perfect precision for all digits. Our new model outperforms other CNN-based models in accuracy and precision. This work offers valuable insights into engraved digit recognition and provides potential implications for designing more accurate and efficient recognition models in various applications.

An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta

The smart environmental management system proposed in this work offers a new approach to environmental monitoring by utilizing data from IoT stations and MODIS satellite imagery. The system is designed to be deployed in vast regions, such as the Mekong Delta, with low building and operating costs, making it a cost-effective solution for environmental monitoring. The system leverages telemetry data collected by IoT stations in combination with MODIS MOD09GA, MOD11A1, and MCD19A2 daily image products to develop computational models that calculate the values land surface temperature (LST), 2.5 and 10 (µm) particulate matter mass concentrations (PM2.5 and PM10) in areas without IoT stations. The MOD09GA product provides land surface spectral reflectance from visible to shortwave infrared wavelengths to determine land cover types. The MOD11A1 product provides thermal infrared emission from the land surface to compute LST. The MCD19A2 product provides aerosol optical depth values to detect the presence of atmospheric aerosols, e.g., PM2.5 and PM10. The collected data, including remote sensing images and telemetry sensor data, are preprocessed to eliminate redundancy and stored in cloud storage services for further processing. This allows for automatic retrieval and computation of the data by the smart data processing engine, which is designed to process various data types including images and videos from cameras and drones. The calculated values are then made available through a graphic user interface (GUI) that can be accessed through both desktop and mobile devices. The GUI provides real-time visualization of the monitoring values, as well as alerts to administrators based on predetermined rules and values of the data. This allows administrators to easily monitor the system, configure the system by setting alerting rules or calibrating the ground stations, and take appropriate action in response to alerts. Experimental results from the implementation of the system in Dong Thap Province in the Mekong Delta show that the linear regression models for PM2.5 and PM10 estimations from MCD19A2 AOD values have correlation coefficients of 0.81 and 0.68, and RMSEs of 4.11 and 5.74 µg/m3, respectively. Computed LST values from MOD09GA and MOD11A1 reflectance and emission data have a correlation coefficient of 0.82 with ground measurements of air temperature. These errors are comparable to other models reported in similar regions in the literature, demonstrating the effectiveness and accuracy of the proposed system.

Retrieval of the aerosol extinction coefficient from scanning Scheimpflug lidar measurements for atmospheric pollution monitoring

In this work, an 808-nm Scheimpflug lidar (SLidar) system, employing a laser diode as the light source and an image sensor as the detector, has been utilized for practical applications of air pollution monitoring in urban area. A comprehensive retrieval method has been proposed and developed for reliable and automatic retrieval of the aerosol extinction coefficient (AEC) from near-horizontal scanning SLidar measurements. The characteristics of the pixel-intensity curves measured by the SLidar system have been statistically examined, and a quality control process is developed to eliminate abnormal lidar profiles. An improved Douglas-Pucker algorithm has been utilized for the determination of the boundary value of the AEC. The AEC profile is thus obtained according to the Klett method with the boundary value as the input. Meanwhile, an angular iterative retrieval (AIR) algorithm has been developed with the AECs at neighboring scanning angles as references, which is able to significantly improve the retrieval reliability of the AEC for each scanning angle. Comparison studies with a local pollution monitoring station have also been carried out to verify the present retrieval method. Besides, the Scheimpflug lidar technique in combination with the retrieval method has shown promising performance in tracking pollution sources in urban area.

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters.

Thrombolysis treatment for acute ischaemic stroke can lead to better outcomes if administered early enough. However, contraindications exist which put the patient at greater risk of a bleed (e.g. recent major surgery, anticoagulant medication). Therefore, clinicians must check a patient's past medical history before proceeding with treatment. In this work we present a machine learning approach for accurate automatic detection of this information in unstructured text documents such as discharge letters or referral letters, to support the clinician in making a decision about whether to administer thrombolysis. We consulted local and national guidelines for thrombolysis eligibility, identifying 86 entities which are relevant to the thrombolysis decision. A total of 8,067 documents from 2,912 patients were manually annotated with these entities by medical students and clinicians. Using this data, we trained and validated several transformer-based named entity recognition (NER) models, focusing on transformer models which have been pre-trained on a biomedical corpus as these have shown most promise in the biomedical NER literature. Our best model was a PubMedBERT-based approach, which obtained a lenient micro/macro F1 score of 0.829/0.723. Ensembling 5 variants of this model gave a significant boost to precision, obtaining micro/macro F1 of 0.846/0.734 which approaches the human annotator performance of 0.847/0.839. We further propose numeric definitions for the concepts of name regularity (similarity of all spans which refer to an entity) and context regularity (similarity of all context surrounding mentions of an entity), using these to analyse the types of errors made by the system and finding that the name regularity of an entity is a stronger predictor of model performance than raw training set frequency. Overall, this work shows the potential of machine learning to provide clinical decision support (CDS) for the time-critical decision of thrombolysis administration in ischaemic stroke by quickly surfacing relevant information, leading to prompt treatment and hence to better patient outcomes.

Diverging climate response of corn yield and carbon use efficiency across the U.S.

In this paper, we developed an open-source package to analyze the overall trend and responses of both carbon use efficiency (CUE) and corn yield to climate factors for the contiguous United States. Our algorithm enables automatic retrieval of remote sensing data through the Google Earth Engine (GEE) and U.S. Department of Agriculture (USDA) agricultural production data at the county level through application programming interface (API). Firstly, we integrated satellite products of net primary productivity and gross primary productivity based on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and climatic variables from the European Centre for Medium-Range Weather Forecasts. Secondly, we calculated CUE and commonly used climate metrics. Thirdly, we investigated the spatial heterogeneity of these variables. We applied a random forest algorithm to identify the key climate drivers of CUE and crop yield, and estimated the responses of CUE and yield to climate variability using the spatial moving window regression across the U.S. Our results show that growing degree days (GDD) has the highest predictive power for both CUE and yield, while extreme degree days (EDD) is the least important explanatory variable. Moreover, we observed that in most areas of the U.S., yield increases or stays the same with higher GDD and precipitation. However, CUE decreases with higher GDD in the north and shows more mixed and fragmented interactions in the south. Notably, there are some exceptions where yield is negatively correlated with precipitation in the Missouri and Mississippi River Valleys. As global warming continues, we anticipate a decrease in CUE throughout the vast northern part of the country, despite the possibility of yield remaining stable or increasing.