Evaluation Of Scenarios Research Articles

Radiation-Induced Brain Necrosis: The Role of The PSMA PET/CT Evaluation - Case Report

This is a case of a 69-year-old patient with Prostate Cancer (PCa) who underwent PSMA PET in three different evaluation scenarios: Biochemical Relapse (BCR), response assessment and differential diagnosis between Radiation-induced Brain Necrosis (RBN), and neoplastic tissue viability. PSMA PET showed superiority compared to conventional imaging exams in all scenarios and impacted on the management of the patient.

The Global Research Trends on Intrinsic Capacity of Older Adults: A Bibliometric and Visual Analysis of Papers Published During 2015-2023.

The concept of intrinsic capacity (IC) revolves around healthy aging and active aging. Since the Introduction of the concept by the World Health Organization in 2015, a series of studies have been conducted by scholars from multiple fields. However, no bibliometric analysis has systematically investigated this issue. We aim to identify the current landscape and frontier trends of scientific achievements on IC in older adults through bibliometric approaches. Quantitative analysis of publications relating to IC in older adults from 2015 to 2023 was interpreted and graphed through the Web of Science Core Collection database on December 5, 2023. A variety of quantitative variables was analyzed, including publication and citation counts, H-index, and journal citation reports. Co-authorship, citation, co-citation, and co-occurrence analyses were performed for countries/regions, institutions, authors, and keywords using the VOSviewer and CiteSpace. A total of 952 original and review articles in English were identified. The European countries possessed an absolute advantage in this field. The most contributive institution was the University of São Paulo. The most productive author is Cesari Matteo from France, followed by Qaisar Rizwan from the United Arab Emirates. However, a relatively low level of research cooperation existed between institutions and authors. Important topics mainly include the connotations, theoretical framework models, evaluation, screening tools, and application scenarios of IC. Among the promising hotspots, "biological aging", "ICOPE", "Covid-19", "prevention", "inflammation", "caf22", "prevalence", and "randomized controlled trial" displayed relatively latest average appearing year. Global trends indicate a growing scientific output on IC in older adults, and developed countries are leading the way. There is still room for improvement in research team collaboration. The focus gradually shifts from theoretical research to empirical research. It is recommended to pay attention to the latest hot spots, such as "biological aging", "ICOPE implementation", "post-COVID-19 syndrome", and "biomarkers".

BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge

BAGESS: A Software Module Based on a Genetic Algorithm to Sequentially Order Load-Balancing Evaluation Scenarios Over Smartphone-Based Clusters at the Edge

Research on the Testing and Evaluation of Active Safety System for Turning Cars in Two wheeler Traffic Scenarios

China In-depth Traffic Accident Survey (CIDAS) shows that two wheeler has the highest accident fatality rate, more than 38%, which is far more than cars and pedestrians. In this paper, the characteristics of traffic accidents in China, the types of two wheeler, and traffic characteristics of two wheeler are taken into account. There are typical hazardous scenarios in the case of crossing, longitudinal, turning, mixed, etc. In the existing collision scenarios design of cars and two wheeler, the testing methods of crossing and longitudinal have been basically mature. Therefore, combining with the functional testing of the current automobile active safety system, to recurrent the traffic scenarios of two wheeler objectively, the active safety test and evaluation scenarios of turning cars under the two wheeler traffic scenarios are designed. Test paths, test speeds, and impact position are proposed. it complements the test scenarios for active safety function.

OEC[sbnd]Net: Optimal feature selection-based email classification network using unsupervised learning with deep CNN model

The escalating prevalence of email communication in global interactions underscores the critical need for robust security measures. However, the existing methods to safeguard email communication, including spam, phishing, and ham emails, have exhibited suboptimal classification performance and inadequate security. So, this work introduces the Optimized Email Classification Network (OECNet) to categorize emails and mitigate security risks effectively. Leveraging a hybrid dataset comprising spam, phishing, and ham email information, we perform meticulous preprocessing to ensure data uniformity. Our approach incorporates principal component analysis (PCA) to extract email-specific pattern properties, enhancing feature discernment through eigenvalue analysis. Inspired by natural swarm behaviours, we employ Particle Swarm Optimization (PSO) to select highly correlated features from PCA outcomes, optimizing the model's efficacy. The OECNet uses PSO-optimised features to integrate a Deep Learning Convolutional Neural Network (DLCNN) model for classification. This novel methodology surpasses traditional methods, yielding an accuracy of 98.43 %, precision of 97.78 %, recall of 96.41 %, and an impressive F1-score of 97.07 %. The proposed OECNet advances email classification technology and establishes a robust security framework against malicious intrusions in global communication. The hybrid dataset incorporates UCI, CSDMC, and SpamAssassin information, ensuring comprehensive coverage and realistic scenarios for effective model training and evaluation.

A digital twin model for evidence-based clinical decision support in multiple myeloma treatment.

The treatment landscape for multiple myeloma (MM) has experienced substantial progress over the last decade. Despite the efficacy of new substances, patient responses tend to still be highly unpredictable. With increasing cognitive burden that is introduced through a complex and evolving treatment landscape, data-driven assistance tools are becoming more and more popular. Model-based approaches, such as digital twins (DT), enable simulation of probable responses to a set of input parameters based on retrospective observations. In the context of treatment decision-support, those mechanisms serve the goal to predict therapeutic outcomes to distinguish a favorable option from a potential failure. In the present work, we propose a similarity-based multiple myeloma digital twin (MMDT) that emphasizes explainability and interpretability in treatment outcome evaluation. We've conducted a requirement specification process using scientific literature from the medical and methodological domains to derive an architectural blueprint for the design and implementation of the MMDT. In a subsequent stage, we've implemented a four-layer concept where for each layer, we describe the utilized implementation procedure and interfaces to the surrounding DT environment. We further specify our solutions regarding the adoption of multi-line treatment strategies, the integration of external evidence and knowledge, as well as mechanisms to enable transparency in the data processing logic. Furthermore, we define an initial evaluation scenario in the context of patient characterization and treatment outcome simulation as an exemplary use case for our MMDT. Our derived MMDT instance is defined by 475 unique entities connected through 438 edges to form a MM knowledge graph. Using the MMRF CoMMpass real-world evidence database and a sample MM case, we processed a complete outcome assessment. The output shows a valid selection of potential treatment strategies for the integrated medical case and highlights the potential of the MMDT to be used for such applications. DT models face significant challenges in development, including availability of clinical data to algorithmically derive clinical decision support, as well as trustworthiness of the evaluated treatment options. We propose a collaborative approach that mitigates the regulatory and ethical concerns that are broadly discussed when automated decision-making tools are to be included into clinical routine.

Сonceptual basis for building a base of models for determining the value of a construction company's information assets

An actual area of research in the field of increasing the effectiveness of the functioning of modern information and communication technologies (ICT) is the principles and methods of establishing the value of information assets (IA) in order to effectively choose means of their protection.
 The object of research of this work is the decision support system (DSPR) for establishing the value of the information asset of the information and communication technologies of the construction company.
 The purpose of the study is to determine the conceptual foundations of building a base of system models, which would allow creating a flexible dynamic decision support system for the implementation of decision-making processes, having a flexible structure that provides for the possibility of implementing alternative scenarios (options) for establishing the value of information assets.
 The principles and conceptual foundations of building a base of models, a decision-making support system with a flexible structure, configured for the possibility of implementing alternative scenarios of expert evaluation, depending on the degree of certainty and availability of data, are proposed. As a conceptual basis, the structure of the model base, alternative and scenario modeling of decision-making processes, a classifier of scenario formation conditions, a database of models in the form of a class diagram, a multivariate process model of information technology for establishing the value of information assets in the form of a set of hierarchical process models of alternative network modeling are considered.
 In the proposed approach, increasing the probability of the obtained results is achieved due to the fact that the user is given the opportunity to choose the most appropriate and effective evaluation scenario, taking into account the goals and objectives of the evaluation, the availability and degree of certainty of the basic data, the existing resource, time and information limitations when making decisions.

A GIS-Based Damage Evaluation Method for Explosives Road Transportation Accidents

The road transportation of explosives is highly concerning due to its substantial impact on social safety. For the safety management of explosive transportation, e.g., transport route planning and emergency rescue, explosion consequence evaluation is of paramount importance. The consequence evaluation of explosion accidents is affected by many factors, especially spatial features, such as the location of transport vehicles, the distribution of buildings, and the presence of individuals around the road, etc. However, there is still a lack of quantification methods for building damage evaluation, human casualty evaluation that considers real-time population density, and efficient interactive damage evaluation methods. In this paper, we formalize three typical scenarios of damage evaluation for explosive road transportation accidents, i.e., explosion point-based, road segment-based, and route-based damage evaluation. For each scenario, we propose a Height-aware Hierarchical Building Damage (HHBD) model and a Shelter-aware Human Casualty (SHC) model for building damage evaluation and human casualty evaluation, respectively. We also develop a GIS-based interactive visualization platform that integrates multi-source geospatial data and that enables efficient geospatial computation. In addition, a case study of liquefied natural gas (LNG) transportation in Wuhan is demonstrated in order to verify the effectiveness and efficiency of the proposed system. The research results can support the decision-making process of explosive transportation safety warnings and emergency rescue.

China's Policy Actions to Lead Teacher Development With Evaluation Reform

Purpose This study introduces a policy with great strategic significance and far-reaching impact by analyzing the background, measures, and future development trends of teacher evaluation reform in China. Design/Approach/Methods This study primarily conducts a policy text analysis of the section on teacher evaluation of the Overall Plan for Deepening the Reform of Educational Evaluation in the New Era. Findings The Overall Plan for Deepening the Reform of Educational Evaluation in the New Era was drafted to enhance the quality of Chinese teachers and address many problems in teacher evaluation. It comprised four sections: teachers’ professional ethics, teaching effectiveness, evaluation models and methods, and honorary titles. The policy has distinctive features such as the high status and authority of the issuing body, a holistic and systematic nature, and an orientation toward practical issues. This article proposes three major policy foresights: promoting implementation through the force of political trends, giving schools autonomy in teacher evaluations, and implementing progressive reforms. Originality/Value This study conducted a specialist analysis of the policy in combination with the real scenario and institutional environment of Chinese teacher evaluation, which could encourage international peers to better understand Chinese teacher evaluation policies and promote policy learning and dissemination internationally.

Delphi-based visual scenarios: An innovative use of generative adversarial networks

In the Futures Studies context, the Delphi-based scenario (DBS) is a valuable method for setting future-oriented strategies and actions by gathering expert opinions in multiple iterative rounds, in order to make better decisions in the present. However, one of the main challenges is to find a suitable representation of the scenarios both for evaluation and decision-making. The development of future scenarios requires high creativity and simple textual descriptions could not be enough to grab the attention of the experts and decision-makers, due to a probable lack of communication and/or technical language. Since the textual descriptions concern the future, the human mind may have difficulty imagining events that have not already happened. With the spread of AI models, different implementations can support the DBS process, such as the use of Generative Adversarial Networks (GANs) to generate specific images starting from the narratives of the scenarios. In this paper, we propose a new method for the development of Delphi-based Visual Scenarios (DBVS) adopting GANs, with the aim to support the experts and decision-makers in planning and communication, in order to have immediate images of what the future could be like. We apply this method with regard to the future of Italian families.

A digital twin-based adaptive optimization approach applied to waste heat recovery in green steel production: Development and experimental investigation

Renewable-dominated power grids will require industry to run their processes in accordance with the availability of energy. At the same time, digitalization introduces new possibilities to leverage the untapped optimization potential to provide this flexibility. Mathematical optimization methods such as mixed-integer linear programming (MILP) are widely used to predict optimal operation plans for industrial systems. MILP models are difficult to adapt, but the viability of the predicted plans relies on accurate underlying models of the actual behavior. New automation paradigms, such as the digital twin (DT), can overcome these current drawbacks. In this work, we present the implementation and experimental evaluation of several micro-services on a standardized five-dimensional DT platform that automate MILP model adaption and operation optimization. These micro-services guarantee that, (1) deviations between the physical entity and its virtual entity models are detected, (2) the models are adapted accordingly, (3) subsequently linearized to suit the MILP approach and (4) used for live operational optimization. These novel services and DT workflows that orchestrate them were experimentally tested with a packed bed thermal energy storage (PBTES) test rig that acts as a physical entity. A waste heat recovery use case in steel production is used as the evaluation scenario. While the model error of a static simulation model would increase to 60% over 7 days of operation, the model error remains well below 25% as a result of successful model adaption. The prediction error of the optimization model remains in a typical magnitude of 10 to 20% during the evaluation period, despite the degradation of the PBTES power.

GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations

This study explores how ChatGPT interprets information through the lens of Construal Level Theory (CLT). The findings show that ChatGPT exhibits an abstraction bias, generating responses consistent with a high-level construal. This abstraction bias results in ChatGPT prioritising high-level construal features (e.g., desirability) over low-level construal features (e.g., feasibility) in consumer evaluation scenarios. Thus, ChatGPT recommendations differ significantly from traditional results based on human decision-making. Applying CLT concepts to large language models provides essential insights into how consumer behaviour may evolve with the increasing prevalence and capability of AI and offers many promising avenues for future research.

Is Robust Optimization Essential When Planning Pencil Beam Scanned Proton Therapy for Intracranial Lesions?

Creating intensity modulated proton therapy (IMPT) plans usually involves a robust optimization step to account for uncertainties in proton range and positioning instead of using the PTV margins typically seen in photon IMRT planning. Because robust optimization adds significant planning time per iteration, and proton planning typically involves many iterations to obtain an optimal plan, this project evaluates whether a PTV approach can be used to more efficiently create plans for intracranial lesions by comparing plan quality from both approaches to determine if they produce equivalent plans. Five patients with intracranial lesions treated with IMPT at our center were randomly chosen for this study. 3-4 beams were used to treat CTVs ranging between 46 and 246 cc. Patients were treated on a Mevion S250i single-vault proton machine with Hyperscan. Static blocks (7mm conformed to the CTV) were used for all cases and plans were created in a treatment planning system using robustness criteria of 3mm (position uncertainty) and 3.5% (range uncertainty). For each patient, the CTV was uniformly grown by 3 mm to create a PTV. The optimization criteria used in the clinical plan were used as baseline to create two plans - one using robust optimization for CTV coverage and one without robust optimization for PTV coverage. A script was used to time two otherwise identical optimization runs. All plans were normalized so that the prescription was delivered to 95% of the CTV. The plan was robustly evaluated under conditions of 3.5% range uncertainty and 2mm positional uncertainty. For each nominal plan, the CTV dose heterogeneity and conformity as well as the following dose metrics were collected: CTV D99% and D98%, Normal Brain V100%, V90%, V80%, V50% as well as overall plan Dmax (to 0.03cc). For each plan, the minimum CTV D99 and maximum Brain Dmax were also collected for the robust evaluation scenarios. All collected metrics were compared between the robust CTV-based and non-robust PTV-based plans using paired t-tests with 5% significance. For the five cases investigated here, all dosimetric metrics investigated were not significantly different between the CTV-based and PTV-based plans except for the plan maximum dose (CTV-based: 104.6% Rx - 110.0% Rx, PTV-based: 105.6% Rx-110.6% Rx, p = 0.029). The optimization times were also significantly different, averaging 1532 s for CTV-based plans versus 252 s for the PTV-based plans (p = 0.004). For the plans investigated here, non-robust PTV planning approach creates plans of very similar quality to a robust CTV-based plan, while having significantly shorter planning times.

Management effectiveness of Nature Conservation Units in southern Brazil

The implementation of protected areas, in particular, nature Conservation Units (CUs), is a conservation strategy recognised worldwide. However, these territories require efficient management to achieve their conservation goals. When the management of CUs is deficient, it results in damage to their own goals, affecting biodiversity and ecological processes, as well as causing social and economic impacts. In this context, we evaluated the management effectiveness of 11 integral Conservation Units of nature in the State of Rio Grande do Sul, southern Brazil, through interviews, visits to these CUs and a review of their official documents. For this analysis, we used the adapted method of Effectiveness of Management of Protected Areas (EMAP), which was analysed using a Likert scale with five levels, eight scopes, 73 indicators and 65 evaluation scenarios. Ninety-one percent of the CUs assessed in southern Brazil by the EMAP method oscillated from average to very unsatisfactory efficacy and low management effectiveness: 18% of the CUs had a very unsatisfactory quality of management, 37% unsatisfactory, 36% average and only 9% high or satisfactory. Moreover, the CUs did not fulfil the main objectives for which they were created. In this context, we recommended a series of actions to be applied for CU improvement, such as the adoption of a quali-quantitative evaluation model for the units, through a mathematical model; increase in staff; training teams and managers; improvement of CU infrastructure and inputs; regular budget allocation; land regularisation, implementation of consultative councils and urgent review of management plans.

Environmental and behavioral effects on pregnancy rate of Nellore cows synchronized to fixed-time artificial insemination protocol: multivariate analysis

The aim of the study was to evaluate the influence of behavioral reactivity, body condition score (BCS), corral temperature (CT), difficulty and time of insemination on pregnancy rate of Nellore females raised in the Pantanal area and submitted to a fixed-time artificial insemination (FTAI) protocol. Thus, 167 females, kept in an extensive grazing system were evaluated. The variables recorded were BCS, escape velocity (EV), CT, insemination difficulty (ID), chute behavior (CB), insemination time (IT) and pregnancy rate. Initially, Fisher's Exact Test was performed to verify differences between expected and observed for BCS, CB, EV, ID, IT and CT, in which the influence of each variable on pregnancy rate was individually analyzed and it was not significant influence of these variables was observed (P<0.05). Subsequently, the result of the pregnancy diagnosis (success = 1 and failure = 0) was used to study the probability of pregnancy. Multivariate analysis was used to assess the influence of several characteristics together on the conception rate, creating different evaluation scenarios. In none of the scenarios extracted from the cluster analysis was there a statistically relevant influence on the number of pregnancies. Under the conditions in which the study was conducted, there was no influence of corral temperature, reactivity, time and difficulty of insemination, body condition score and animal category on pregnancy rate of bovine females submitted to a FTAI protocol.

Performance evaluation and control scenarios for targeted heat injection and extraction in an existing geothermal borehole field in Norway

This work presents the calibration, validation, and analysis of a borehole thermal energy storage (BTES) in building performance simulation using operational data from an existing borehole field in Kalnes, Norway. The data used in this study comprises the results of a thermal response test as well as operational data from the borehole field, which is used to cover the heating and cooling load of a hospital complex. The first part of this study focuses on the calibration and validation of the borehole field model using the IDA ICE software. Then, the validated model is used to explore the impact of different operational strategies in which the charging/discharging of the three sections of the borehole field are prioritized in different orders and compared to the most recent operational strategy. This analysis is carried out on a single year and a 20-year perspective to evaluate long-term temperature trends. This investigation aims to evaluate the current and future impacts of the existing operational strategy and whether it could be improved given that the real-life system struggles with below zero brine temperature at the end of the heating season. The simulation results show that the outgoing brine temperature in each borehole section is strongly dependent on the mass flow rate used in the BTES but that the temperature in an individual section had little impact on the neighboring one. When a section was prioritized by the control logic for heat extraction or injection (both in terms of order and mass flow), there was a notable increase or decrease in the outgoing brine temperature, indicating that it was possible to have targeted heat injection/extraction. In the 20-year operational horizon, the simulation results predicted a gradual warming trend of the outgoing brine temperature of approximately 1 °C due to the additional heat injected in all three sections and regardless of the scenario. The study shows that the most recently implemented operational strategy, in which all sections are charged and discharged simultaneously, is the most viable operation scenario for the borehole thermal energy storage at Kalnes, thus confirming previous findings in literature. Since none of the sections had a superior storage or heat regeneration capacity, prioritizing sections would only lead to more significant temperature swings in the ground. On the other hand, the current operation strategy leads to an overall higher temperature in the ground and reduces the risk of low outgoing brine temperatures.

Decision-Making Model for Evaluating Joint Venture Contractors in Construction of Complex Infrastructure Megaprojects

Due to the complexity of infrastructure megaprojects, using joint venture contractors (JVCs) is an effective method to deliver projects. However, this process is constrained by several factors, including the quality of competing JVCs and knowledge level of decision-makers (DMs). However, existing decision-making methods for megaprojects and JVC selection lack comprehensive JVC evaluation criteria and cannot handle decision-making scenarios using incomplete data. To address these issues, in this study, we propose a novel model based on a hesitant fuzzy soft set and a linear programming techniques for multidimensional analysis of preferences called LINMAP, which can process incomplete decision data and improve decision accuracy. Moreover, a set of criteria was constructed from four dimensions (economy, society, environment, and cooperation), which upholds the sustainable development and internal cooperation of the built environment. Finally, by applying the approach to a complex infrastructure megaproject and conducting a sensitivity analysis, the feasibility and effectiveness of the proposed model were verified. Research shows that DMs in different specialized areas choose different criterion subsets, which leads to different decision-making results. For one, allowing DMs to choose a subset of the criteria can improve the accuracy of a decision and reduce the accompanying risks; further, a large gap exists between partial and global evaluations, so partial and global consistence must be included in the context of multicriteria decision-making; finally, cooperation is the premise of competition, i.e., good cooperation has a positive effect on competition, so the sustainable development of a JVC needs to consider not only economic, environmental, and social factors but also cooperation. This study contributes to the knowledge domain of multicriteria decision-making, extends the knowledge domain in terms of clarifying complicated evaluation scenarios in infrastructure megaprojects, and renders practical managerial implications for future complex evaluation processes in infrastructure megaprojects.

Bridge Evaluation Based on Identified Influence Lines and Influence Surfaces: Multiple-Scenario Application

Bridge influence lines (BILs) and bridge influence surfaces (BISs) are inherent static parameters of bridges which can be extracted from moving vehicle-induced bridge responses. Compared with dynamic parameters, these parameters are directly related to the stiffness and internal forces in each cross-section of a bridge therefore can be considered as an effective bridge metamodel. To accelerate the engineering practice of BIL- and BIS-based bridge evaluation, this paper first briefly reviews the current BIL and BIS field test and identification methods. Then, the bridge evaluation guidelines of China and the United States are introduced as the basis of the evaluation methods. Engineering application scenarios for bridge evaluation, including permit load verification, performance degradation checking, and load carrying capacity evaluation, are summarized, and a multiple-scenario bridge evaluation method is established. At the end of this paper, an evaluation example of a four-span continuous bridge is presented to illustrate the application procedure and verify the effectiveness of the proposed method. The outcomes of this paper provide a promising application method of field test BILs and BISs, which may help bridge engineers more effectively use these parameters in engineering practice.

Sentinel-1 Time Series for Predicting Growing Stock Volume of Boreal Forest: Multitemporal Analysis and Feature Selection

Copernicus Sentinel-1 images are widely used for forest mapping and predicting forest growing stock volume (GSV) due to their accessibility. However, certain important aspects related to the use of Sentinel-1 time series have not been thoroughly explored in the literature. These include the impact of image time series length on prediction accuracy, the optimal feature selection approaches, and the best prediction methods. In this study, we conduct an in-depth exploration of the potential of long time series of Sentinel-1 SAR data to predict forest GSV and evaluate the temporal dynamics of the predictions using extensive reference data. Our boreal coniferous forests study site is located near the Hyytiälä forest station in central Finland and covers an area of 2500 km2 with nearly 17,000 stands. We considered several prediction approaches and fine-tuned them to predict GSV in various evaluation scenarios. Our analyses used 96 Sentinel-1 images acquired over three years. Different approaches for aggregating SAR images and choosing feature (predictor) variables were evaluated. Our results demonstrate a considerable decrease in the root mean squared errors (RMSEs) of GSV predictions as the number of images increases. While prediction accuracy using individual Sentinel-1 images varied from 85 to 91 m3/ha RMSE, prediction accuracy with combined images decreased to 75.6 m3/ha. Feature extraction and dimension reduction techniques facilitated the achievement of near-optimal prediction accuracy using only 8–10 images. Examined methods included radiometric contrast, mutual information, improved k-Nearest Neighbors, random forests selection, Lasso, and Wrapper approaches. Lasso was the most optimal, with RMSE reaching 77.1 m3/ha. Finally, we found that using assemblages of eight consecutive images resulted in the greatest accuracy in predicting GSV when initial acquisitions started between September and January.

A novel uncertainty analysis method to improve the accuracy of agricultural grey water footprint evaluation considering the influence of production conditions

Agricultural grey water footprint (GWF) is an important index for quantitatively describing the impact of agricultural production on water environment, which can help evaluate regional water pollution risk (WPR). However, changes in agricultural production conditions lead to strong uncertainties in agricultural GWF. Therefore, advanced methods to deal with these uncertainties for improving the accuracy of regional WPR assessment are required. By combining min–max/max-max programming models with a GWF assessment framework, an integrated uncertainty analysis method for agricultural GWF affected by production conditions was proposed in this study. Based on the method, extreme scenarios of GWF evaluation were established to obtain lower and upper bounds of agricultural GWF. The proposed method was applied in the Guangdong Province in South China, and the following results were obtained. Agricultural GWFs in the Guangdong Province were [124.67, 237.37], [110.63, 204.90], and [104.04, 178.01] Gm3 in 2005, 2010, and 2015, respectively. Different contributing factors of agricultural GWF were shown in the minimum and maximum GWF scenarios. For crop planting, the contribution of dryland and paddy fields to GWF was larger than that of garden land in the minimum GWF scenario, whereas the contribution of garden land was greatest in the maximum GWF scenario. For livestock and poultry breeding, the contribution of livestock raised under dry cleaning condition to the GWF was larger than that under flushing condition in the minimum GWF scenario, whereas the opposite situation was observed in the maximum GWF scenario. Based on the interval values of agricultural GWF, the WPR of the cities of Zhanjiang, Maoming, Yunfu, Chaozhou, Foshan, and Dongguan were identified as unacceptable. By identifying the uncertainties within agricultural GWF evaluation induced by the variability of production conditions, the proposed method can provide helpful information for agricultural planning in the context of sustainable agricultural development.