A comprehensive comparison of advanced metaheuristic photovoltaic maximum power tracking algorithms during dynamic and static environmental conditions.

This study introduces a novel technique for achieving the global peak (GP) in solar photovoltaic (PV) systems under partial shadowing conditions (PSC) using the Dandelion Optimizer Algorithm (DOA), inspired by the dispersal of dandelion seeds in the wind. The proposed approach aims to enhance the power generation efficiency of PV systems across various scenarios, including dynamic uniform, dynamic PSCs, static uniform irradiances, and static PSCs. The proposed approach improves tracking efficiency, provides non-oscillatory steady-state responses, and reduces transients as well as enhancing the dynamic performance of the whole system. Simulation and hardware-in-loop (HIL) experiments demonstrate that the DOA outperforms several state-of-the-art techniques, such as hybrid grey wolf optimizer since-cosine algorithm (HGWOSCA), grasshopper optimization algorithm (GOA), dragonfly optimizer (DFO), particle swarm optimizer with gravitational search (PSOGS), PSO, cuckoo search algorithm (CSA), perturb &observe (P&O), and incremental conductance (INC), achieving average efficiencies of 99.93 %, 88.84 %, 94.48 %, 87.12 %, 88.05 %, 94.79 %, 93.82 %, 85.25 %, and 77.93 %, respectively. These results underscore the DOA's effectiveness in improving maximum power point tracking (MPPT) performance in solar arrays, particularly under challenging dynamic PSC conditions.

Global Distribution Prediction of Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae) Insights from the Optimised MaxEnt Model.

Cyrtotrachelus buqueti Guer is a major pest affecting bamboo forests economically, causing significant damage to bamboo forests in Sichuan Province, China. To understand how C. buqueti responds to future climate conditions, an optimized Maximum Entropy Model (Maxent) was used to simulate the potential global distribution patterns of C. buqueti under current climate conditions and three different future climate scenarios and to analyze the dominant factors influencing its distribution. The results indicate that Bio18 (precipitation of the warmest quarter), Bio04 (temperature seasonality), Bio06 (minimum temperature of the coldest month), and Bio02 (mean diurnal temperature range) are the main environmental factors affecting the distribution of this species. The global area of high-suitability habitats for C. buqueti is 9.00 × 104 km2, primarily distributed in China. Under three different future climate scenarios, there are varying degrees of expansion in both the total suitable habitat and the medium-suitability areas for C. buqueti. Under the SSP5-8.5 scenario, the medium-suitability area of the species increases the most, reaching 9.83 × 104 km2. Additionally, these findings can serve as a reference for developing and implementing control strategies, assisting relevant authorities in more effectively managing and controlling this pest, and mitigating its potential threats to bamboo forest ecosystems and economies.

Clinical Judgment Item Development for Emergency Medical Service Clinicians.

OBJECTIVES: While clinical judgment is vital for all clinicians, it is not clearly assessed in initial or continuing emergency medical services (EMS) education due to unclear definitions. Recently, clarity of this concept has been provided through the development of a theoretical framework for clinical judgment in EMS that considers the broad and evolving nature of prehospital care delivery. METHODS: To facilitate standardization of clinical judgment assessments, in this educational practice review we present a template for item development leveraging the new framework. Developed with input from EMS clinicians, educators, and expertise from the nursing field with experience in clinical judgment item development, this template can be used to support item generation for a specific event phase (e.g., en route, scene, and post scene) in a clinical scenario. RESULTS: We provide a transparent and reproducible template for item generation for clinical judgment assessments evaluating the six basic cognitive reasoning steps of recognizing cues, analyzing cues, defining a hypothesis, generating solutions, taking action, and evaluating the outcomes of those actions. CONCLUSIONS: This template allows for generation of items for each EMS event phase that can be repeated serially for any combination of prehospital clinical situations.

How I Do It: Using ChatGPT to Create Clinical Scenarios for Surgical Learning and Oral Boards Preparation.

OBJECTIVE: To evaluate the use of the large language model ChatGPT to simulate an oral surgical boards examination environment. DESIGN: ChatGPT was asked to create oral surgical boards questions based on a series of clinical scenarios. RESULTS: ChatGPT created clinically relevant oral board-type questions. ChatGPT provided pertinent follow-up questions after the user's response as would occur in an oral examination as well as feedback regarding the user's response. CONCLUSIONS: Chat GPT can simulate an oral boards-style examination of a surgical trainee with a reasonable degree of clinical detail and immediate feedback. It may be a useful as a curricular tool and for self-education and board preparation.

Techniques and Instruments for Assessing and Reducing Risk of Exposure to Nanomaterials in Construction, Focusing on Fire-Resistant Insulation Panels Containing Nanoclay.

The paper explains how nano exposure is assessed in the construction field and focuses on the production of fire-resistant insulation panels with nanoclay. Utilizing the commercial ANSYS CFX® software, a preliminary theoretical simulation was conducted on nano exposure in the workplace, which revealed that particle dispersion is primarily driven by diffusion. Panel post-processing through drilling results in the highest inhalation exposure, followed by mixing and grinding activities. Compared to a state of 'no activity', each activity resulted in an exposure increase by a factor of min. 1000. An overall assessment suggests that the use of nanoparticles in construction materials may not significantly heighten workers' exposure to nanopowders when considering particle concentration alone as opposed to using traditional micro-scale materials. However, the issue persists when it comes to blending powders or performing finishing tasks on panels, with concentration levels being significantly higher for drilling, grinding, and mixing powders at 2.4 times above the standard reference value (40,000 particles/cm3); this is unacceptable, even for brief durations. Examination of dermal contact with gloves and masks worn by workers revealed no nanoparticle penetration. Safety measures were proposed for workers based on decision trees to enhance their safety. Ten categories of protection strategies have been devised to combat the impact of nanoparticles, which are tailored to specific technical situations, but they must be modified for various types of nanoparticles despite potential shared health implications.

Sediment load assessments under climate change scenarios and a lack of integration between climatologists and environmental modelers.

Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information.

Immersive medical training: a comprehensive longitudinal study of extended reality in emergency scenarios for large student groups.

Young healthcare professionals and medical graduates often fall short in the practical experience necessary for handling medical emergencies. This can not only lead to strained feelings of inadequacy and insecurity among future physicians and less experienced healthcare providers in general, but also to detrimental outcomes for patients as emergency medicine demands rapid decision-making with low tolerance for errors. New didactic modalities and approaches may be needed to effectively address this shortcoming. Immersive technologies are powerful novel educational tools with untapped potential in medical training, and may be particularly suitable for simulation trainings in the high-stakes field of emergency medicine.Herein, we systematically explored the educational potential of extended reality (XR) technology, particularly virtual reality (VR), in the management of patients presenting as medical emergencies, combining the use of the STEP-VR application with an untethered hardware setup.Importantly, we aimed at studying multiple, large cohorts of senior medical students involving a total of 529 participants and collecting data over a period of two years. We assessed students' acceptance of the training through a modified questionnaire measuring device handling, content complexity, degree of immersion, learning success, and seminar design.Our results show high, sustained acceptance and ease of use across different student cohorts and subgroups, with most students finding XR/VR engaging and beneficial for acquiring emergency medicine skills. Importantly, the prevalence of simulation sickness was minimal. Moreover, no major effect of the head-mounted displays (HMDs) price range was noted with regard to the learning experience. The results underscore the potential of XR/VR capabilities in effectively enhancing medical education, particularly in areas of high-stakes clinical scenarios and emergency care, by providing realistic and reproducible immersive training environments.In summary, our findings suggest that XR/VR-based training approaches could significantly contribute to preparing future physicians for the complexities of emergency medical care, encouraging the integration of such technologies into medical curricula. However, careful consideration must be given to its suitability for all students and the practical challenges of its implementation, highlighting the need for further research to harness its full potential for medical education.

Insight into the impact of environmental factors on heavy metal adsorption by sodium alginate hydrogel: Inspiration on applicable scenarios.

Sodium alginate (SA) emerges as a promising adsorbent for the remediation of heavy metal-polluted wastewater. However, the systematic investigations on how and the extent to which the various compositions in real water matrices impact its performance were essential but rare when considering its use. Here, we explored the effect of common environmental factors on Cu(II) adsorption by an as-synthesized SA-based hydrogel (SAH). The result showed that high concentration of organics (above 10 mg L-1) had a negative influence on heavy metal removal (decreased by 9.45 % at least), while inorganic ion, turbidity and antibiotics at relatively low concentrations exhibited a negligible even promoting effect (increased by 9.8 % with the presence of 5 mg L-1 Nor). Based on above results and corresponding mechanism analyses, the possible applicable and unsuitable scenarios of SAH can be predicted. SAH could be a great candidate for treating heavy metal-polluted water such as river and lake water, while it is not a good option for electroplating or livestock wastewater which contains high concentration of organic matters. Besides, the operating conditions including pH (5.0 for Cu(II), 6.0 for Ni(II)), contact time (24 h), temperature (298 K) et al. were also determined. Overall, this work provides theoretical guidance and operational strategies for promoting the practical application of SA adsorbent in water treatment.

Future scenarios of palliative care in health system of Iran: a multi-method study.

Background: Paying attention to palliative care has accelerated in Iran in the last 10 years. Considering the trend of aging, increasing burden of chronic diseases and increasing health costs, planning and development of palliative care is necessary in the future. This study was conducted with the aim of explaining the alternative scenarios of palliative care in the health system of Iran until the horizon of 2030. Methods: This study was a multi-method scenario planning with a qualitative using multiple methods design, which was conducted in 3 phases in 2018-2020. In the first phase, a list of driving forces was extracted using qualitative interviews and literature review. In the second phase, all factors identified in the previous phase were examined in terms of degree of uncertainty and cross-impact analysis, and two key uncertainties were extracted. In the third phase, based on two key uncertainties, four future scenarios of palliative care were formulated, validated and scenario strategies were presented. Results: The results indicate two uncertainties, including "governance of palliative care in the health system" and "acceptance of palliative care by society," based on which, four scenarios with the names "climbing to the top," "excruciating climb," "edge of the abyss" and "The bottom of the valley" were compiled. Conclusion: The development of palliative care in health system of Iran is faced with serious uncertainties that it is necessary to focus the developmental activities of palliative care on the two axes of acceptance by society and need for coherent governance by considering all the dimensions and influential components by ministry of health. The application of the results of this research can provide reasonable options for effective interventions and implementation of this category of services to the beneficiaries of palliative care.

Bacterial mercury methylation modulated by vitamin B9: An overlooked pathway leads to increased environmental risks.

There has been a serious health and environmental concern in conversion of inorganic mercury (Hg) to the neurotoxin, methylmercury (MeHg) by anaerobic microbes, while very little is known about the potential role of vitamin B9 (VB9) regulator in the biochemical generation of MeHg. This study innovatively investigated bacterial Hg methylation by Geobacter sulfurreducens PCA in the presence of VB9 under two existing scenarios. In the low-complexing scenario, the bacterial MeHg yield reached 68 % higher than that without VB9 within 72 h, which was attributed to free VB9-protected PCA cells relieving oxidative stress, as manifested by the increased expression of Hg methylation gene (hgcAB cluster by 19-48 %). The high-complexing scenario emphasized the intracellular Hg accumulation (38-45 %) after 12 h, as indicated by the increased expression of outer membrane protein-related and mercuric reductase-encoding genes, indicating the inefficient bioavailability of Hg due to a gradual shift from Hg reduction toward Hg0 re-oxidation controlled by competitive ligand exchange. These results suggested that VB9 application significantly raised the potential for bacterial Hg methylation and cellular accumulation, thus proposing insights into the biochemical behaviors of hazardous Hg in farming environments where vulnerable organisms are more possibly co-exposed to higher levels of Hg and VB9.

Robust visual SLAM algorithm based on target detection and clustering in dynamic scenarios.

We propose a visual Simultaneous Localization and Mapping (SLAM) algorithm that integrates target detection and clustering techniques in dynamic scenarios to address the vulnerability of traditional SLAM algorithms to moving targets. The proposed algorithm integrates the target detection module into the front end of the SLAM and identifies dynamic objects within the visual range by improving the YOLOv5. Feature points associated with the dynamic objects are disregarded, and only those that correspond to static targets are utilized for frame-to-frame matching. This approach effectively addresses the camera pose estimation in dynamic environments, enhances system positioning accuracy, and optimizes the visual SLAM performance. Experiments on the TUM public dataset and comparison with the traditional ORB-SLAM3 algorithm and DS-SLAM algorithm validate that the proposed visual SLAM algorithm demonstrates an average improvement of 85.70 and 30.92% in positioning accuracy in highly dynamic scenarios. In comparison to the DynaSLAM system using MASK-RCNN, our system exhibits superior real-time performance while maintaining a comparable ATE index. These results highlight that our pro-posed SLAM algorithm effectively reduces pose estimation errors, enhances positioning accuracy, and showcases enhanced robustness compared to conventional visual SLAM algorithms.

Quantifying future climate extreme indices: implications for sustainable urban development in West Africa, with a focus on the greater Accra region.

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa.

End-to-End Autonomous Driving Decision Method Based on Improved TD3 Algorithm in Complex Scenarios.

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability.

Coordinated Multi-Scenario Optimization Strategy for Park Photovoltaic Storage Based on Master-Slave Game.

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master-slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators' profit, while the lower-level objective is to minimize the user's power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks.

Range dynamics of Anopheles mosquitoes in Africa suggest a significant increase in the malaria transmission risk.

Despite a more than 100-year effort to combat malaria, it remains one of the most malignant infectious diseases globally, especially in Africa. Malaria is transmitted by several Anopheles mosquitoes. However, until now few studies have investigated future range dynamics of major An. mosquitoes in Africa through a unified scheme. Through a unified scheme, we developed 21 species distribution models to predict the range dynamics of 21 major An. species in Africa under future scenarios and also examined their overall range dynamic patterns mainly through suitability overlap index and range overlap index. Although future range dynamics varied substantially among the 21 An. species, we predicted large future range expansions for all 21 An. species, and increases in suitability overlap index were detected in more than 90% of the African continent for all future scenarios. Additionally, we predicted high range overlap index in West Africa, East Africa, South Sudan, Angola, and the Democratic Republic of the Congo under future scenarios. Although the relative impacts of land use, topography and climate variables on the range dynamics depended on species and spatial scale, climate played the strongest roles in the range dynamics of most species. Africa might face an increasing risk of malaria transmissions in the future, and better strategies are required to address this problem. Mitigating climate change and human disturbance of natural ecosystems might be essential to reduce the proliferation of An. species and the risk of malaria transmissions in Africa in the future. Our strategies against their impacts should be species-specific.

Augmenting intensive care unit nursing practice with generative AI: A formative study of diagnostic synergies using simulation-based clinical cases.

BACKGROUND: As generative artificial intelligence (GenAI) tools continue advancing, rigorous evaluations are needed to understand their capabilities relative to experienced clinicians and nurses. The aim of this study was to objectively compare the diagnostic accuracy and response formats of ICU nurses versus various GenAI models, with a qualitative interpretation of the quantitative results. METHODS: This formative study utilized four written clinical scenarios representative of real ICU patient cases to simulate diagnostic challenges. The scenarios were developed by expert nurses and underwent validation against current literature. Seventy-four ICU nurses participated in a simulation-based assessment involving four written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified ICU nurses for accuracy, completeness and response. RESULTS: Nurses consistently achieved higher diagnostic accuracy than AI across open-ended scenarios, though certain models matched or exceeded human performance on standardized cases. Reaction times also diverged substantially. Qualitative response format differences emerged such as concision versus verbosity. Variations in GenAI models system performance across cases highlighted generalizability challenges. CONCLUSIONS: While GenAI demonstrated valuable skills, experienced nurses outperformed in open-ended domains requiring holistic judgement. Continued development to strengthen generalized decision-making abilities is warranted before autonomous clinical integration. Response format interfaces should consider leveraging distinct strengths. Rigorous mixed methods research involving diverse stakeholders can help iteratively inform safe, beneficial human-GenAI partnerships centred on experience-guided care augmentation. RELEVANCE TO CLINICAL PRACTICE: This mixed-methods simulation study provides formative insights into optimizing collaborative models of GenAI and nursing knowledge to support patient assessment and decision-making in intensive care. The findings can help guide development of explainable GenAI decision support tailored for critical care environments. PATIENT OR PUBLIC CONTRIBUTION: Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.

HelmetML: A dataset of helmet images for machine learning applications.

The improper wearing or absence of helmets represents a significant contributing factor to fatal accidents in motorcycle driving. This dataset serves the purpose of detecting whether individuals have correctly or incorrectly worn helmets through camera-based analysis. The Helmet dataset has been curated, comprising a total of 28,736 images featuring various helmet types, including Full-Face, Half-Face, Modular, and Off-Road Helmets, in both correct and incorrect configurations. Captured using an iPhone 13 and Mi10T mobile phones, the images exhibit diverse climatic conditions, ranging from daytime to night-time scenarios. Subsequent to image acquisition, a pre-processing phase was undertaken to standardize the dataset. This involved renaming the images and adjusting their dimensions to a uniform 768 × 576 resolution, after which they were organized into respective folders. The uniqueness of this dataset lies in its incorporation of diverse environmental conditions, comprehensive helmet types, variability in helmet orientations, and its status as a large and balanced dataset, thereby presenting a realistic representation of real-world scenarios. The dataset's utility extends to various machine learning tasks, including image classification, object detection, and pose estimation specifically geared towards helmet recognition. Its scientific value lies in its potential to advance research and development in the realm of safety measures associated with motorcycle helmet usage.

Effectiveness of the flipped classroom method using clinical scenarios and Educational Technology versus Subject-Based Lectures in a gastrointestinal physiology course for medical students.

BACKGROUND: Medical education is evolving towards more practical, active, effective, and student-centered approaches that address the limitations of traditional lecture methods. Recently, the flipped classroom method has been considered to support these reforms. However, research on the use of flipped classroom methods in medical education, particularly related to clinical scenarios and educational technology, is still in its early stages. This study aims to evaluate the effectiveness of the flipped classroom method using clinical scenarios and educational technology versus subject-based lectures in the course of gastrointestinal physiology for medical students. METHODS: A total of 60 medical students participated in this study. The control group (n = 30) received traditional subject-based lectures and participated in question-and-answer sessions. The intervention group (n = 30) received non-attendance educational content and participated in small group discussions based on clinical scenarios. Course satisfaction was measured using an 18-item questionnaire, and learning outcomes were assessed with a 20-question multiple-choice test, corresponding to levels 1 and 2 of Kirkpatrick's model. Data were analyzed using descriptive and analytical statistical tests with SPSS software version 24. RESULTS: The findings indicated that the post-test scores in the intervention group were significantly higher compared to the control group. However, according to the student satisfaction questionnaire, satisfaction was significantly lower in the intervention group compared to the control group. CONCLUSION: This study demonstrated that the flipped classroom method, compared to traditional lectures, improved the learning and performance of medical students at Hamadan University of Medical Sciences in the course of gastrointestinal physiology.

Evaluation of farmland production potential in key agricultural production areas on the Qinghai-Tibet Plateau under multi-scenario simulation.

Predicting changes in future land use and farmland production potential (FPP) within the context of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) is crucial for devising sustainable land use strategies that balance agricultural production and ecological conservation. Therefore, the Huangshui Basin (HSB) in the northeast Qinghai-Tibet Plateau is taken as the study area, and a LUCC-Plus-FPP (LPF) coupling framework based on the SSP-RCP scenarios is proposed to evaluate future land use patterns and FPP changes. On the basis of the predictions of land use changes from 2020 to 2070, the trade-offs in grain production resulting from bivariate changes in farmland and FPP under future scenarios are analyzed. The results indicate that the model has a high simulation accuracy for land use types, with an overall accuracy of 0.98, a kappa coefficient of 0.97, and a figure of merit value of 0.21. Under the SSP245 and SSP585 scenarios, built-up land increases significantly, by approximately 45.89 %. Farmland and grassland conversions contribute the most to increased built-up land. Farmland area consistently decreases by approximately 5 % across all scenarios. The protection of farmland in the study area is difficult to undertake and thus requires much attention. Moreover, under the SSP126 scenario, the FPP of most districts is greater than that in 2020, and the average FPP of the HSB from 2030 to 2070 is greater than that in 2020. In the SSP585 scenario, by 2070, the average FPP of all districts decreases to different degrees compared with that in 2020. Furthermore, the compensated farmland quantities and average FPPs under all the scenarios are significantly lower than the amount of occupied farmland. The results provide a theoretical foundation and data support for farmland protection decision-making and layout optimization in the Qinghai-Tibet Plateau.