Realistic Scenarios Research Articles

A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study.

To develop a deep learning (DL) model for carotid plaque detection based on CTA images and evaluate the clinical application feasibility and value of the model. We retrospectively collected data from patients with carotid atherosclerotic plaques who underwent continuous CTA examinations of the head and neck at a tertiary hospital from October 2020 to October 2022. The model combined ResUNet with the Pyramid Scene Parsing Network (PSPNet) to enhance plaque segmentation. Patient plaques were divided into training, validation, and testing sets in a ratio of 7:1.5:1.5. We analyzed recall (lesion-level sensitivity), sensitivity (patient-level), and precision to evaluate the model's diagnostic performance for carotid plaques. The two stepwise early-stage clinical validation study (Comparison study and Model-human study) was used to simulate real clinical plaque diagnostic scenarios. In total, 647 patients were included in the dataset, including 475 for training, 86 for validation, and 86 for testing. The DL model based on CTA images showed good precision in plaque diagnosis (validation set: precision = 80.49%, sensitivity = 90.70%, recall = 84.62%; test set: precision = 78.37%, sensitivity = 91.86%, recall = 84.58%). In addition, subgroup analysis of the plaque was carried out in the test set. The model had high accuracy in identifying plaques at different locations (Recall: 83.72, 76.32, 89.25, and 83.02%) and with different morphologies (Recall: 86.03, 79.17%). This model also analyzed the results of different types of plaques and showed good to moderate plaque diagnostic accuracy for different plaque types (Recall: 70.00, 86.87, 84.29%). Especially, in the clinical application scenario analysis, the model's diagnostic results for plaques were found to be higher than those of 4 out of 6 radiologists (p < 0.001). Furthermore, in Model-human Real Clinical Scenarios study, we found that the model improved the radiologists' sensitivity in diagnosing plaques. Additionally, the model's diagnostic time for plaques (6 s) was found to be significantly shorter than that all of radiologists (p < 0.001). This AI model demonstrated strong clinical potential for carotid plaque detection with improved clinician diagnostic performance, shortening time, and practical implementation in real-world clinical cases.

Connecting Cities: A Case Study on the Application of Morphological Shortest Paths

Navigatingdensely connected networks can be complex due to the different connection structures present within a network. No explicit algorithms are designed specifically for this navigation, so heuristic approaches and existing network systems are often employed. However, this task can become computationally asymmetrical, as the complexity of creating a representation of the city is lower than the complexity involved in identifying a set of feasible paths in a combinatorial order. This paper extends the applicability of morphological approaches to compute the shortest path in smart cities, driven by the complexity and size of the vital communication infrastructure. As is well known, this communication infrastructure changes dynamically, particularly with the evolving connection paths due to continuous population growth. Consequently, efficient communication trajectories can quickly become obsolete. The challenge of computing the best trajectories to respond more quickly to the growing population comes with high computational complexity. This paper presents an application that uses a discrete algorithm designed to compute the shortest path through a morphological approach. Specifically, it seeks to identify the best trajectory within a densely populated city based on a complex density graph. By incorporating morphological approaches into path-search algorithms, we can define a new family of methods that operate in discrete spaces with a morphological representation, resulting in approaches that have lower computational requirements. Other well-known applications in this context include the delivery of resources, such as managing electrical power consumption or minimizing time delays in resource delivery. This task is essential but classified as an NP problem, making it an appropriate scenario for applying the proposed algorithm to navigate a dense graph. The paper highlights the well-known problem of finding the shortest path as one of the potential applications of the introduced algorithm. The algorithm aims to identify the optimal path trajectory within a graph representing a dense city’s real scenario. This discussion compares and contrasts the proposal with other established approaches, highlighting the advantages and characteristics of the proposed method.

Associations of Exposure to 56 Serum Trace Elements with the Prevalence and Severity of Acute Myocardial Infarction: Omics, Mixture, and Mediation Analysis.

Several studies have reported associations between specific heavy metals and essential trace elements and acute myocardial infarction (AMI). However, there is limited understanding of the relationships between trace elements and AMI in real-life co-exposure scenarios, where multiple elements may interact simultaneously. This cross-sectional study measured serum levels of 56 trace elements using inductively coupled plasma mass spectrometry. We identified individual trace elements linked to AMI using four feature selection methods and evaluated their associations with AMI prevalence and severity through multiple-element logistic regression. Restricted cubic spline analysis was employed to examine non-linear associations. Additionally, we explored the associations between trace element mixtures and AMI prevalence and severity using Bayesian kernel machine regression (BKMR) and element risk score (ERS). Finally, we investigated the potential mechanisms linking trace element exposure to AMI. We detected stable positive associations and linear relationships between Cu and Rb and AMI prevalence and severity. Furthermore, lower Fe concentrations were associated with higher AMI prevalence, while higher Sb concentrations were linked to greater AMI severity. Both BKMR and ERS models indicated positive associations between trace element mixtures and AMI prevalence and severity. Mediation analysis suggested that high-sensitivity C-reactive protein partially mediated the associations between trace elements and AMI prevalence and severity. We provide the first epidemiological evidence of the associations between serum trace element mixtures and AMI prevalence and severity. Under conditions of trace element co-exposure, Cu, Rb, Fe, and Sb were closely associated with AMI. Additionally, our results indicate that hsCRP (inflammation) may be a potential mechanism linking trace elements to AMI.

Spatiotemporal Response Relationships Between Changes in Water Conservation Service and Landscape Pattern in Zoige Plateau, China

ABSTRACTUnderstanding the link between landscape pattern and water conservation service (WCS) is crucial for effective water resource conservation and landscape planning. However, the impact of landscape configuration on WCS remains unclear. This study explored the spatiotemporal relationships between WCS and landscape pattern in the Zoige Plateau (ZP), a vital waterhead of the yellow river, from 1990 to 2020. The results showed that woodland and high‐coverage grassland exhibited the highest WCS capacity and contributed the greatest to total WCS, suggesting the importance of protecting coverage and area of woodland and grassland. From 1990 to 2020, the areas of high‐ and medium‐coverage grasslands significantly decreased, implying adverse effects of landscape composition changes on ZP's WCS during the past few decades. The effects of landscape configuration metrics on WCS exhibited significant variability across years, climate scenarios and regions, highlighting the significant role of climatic and underlying surface. Under realistic climatic scenarios, for greater WCS, in most parts of the western and northern ZP, which negatively correlated with aggregation index, patch density, and mean fractal dimension index but positively correlated with shannon's diversity index, it is better to increase the proportion of large patches, reduce landscape fragmentation, and maintain diverse patch types. Conversely, in southeastern part of the central ZP, where WCS showed an opposite correlation with the aforementioned metrics, maintaining simpler patch type diversity and higher landscape connectivity may be useful. These findings provided important insights for identifying sensitive areas where landscape pattern changes affect WCS and optimizing landscapes to sustain ZP's WCS.

Human Identification System using Images

The present study focuses on the challenges of human long-range recognition with deep learning, and AlexNet performance is considered, being one of the most recognized convolutional neural networks. A specially curated dataset was used to mimic the real world in a more practical and realistic scenario, which involves subjects varying in distances and angles while creating low-resolution issues with variability in lighting and distractions from the environment. The system starts by passing the input dataset through a local Laplacian filter that is utilized to enhance the quality of images before feeding it into a pre-trained AlexNet model. The results reveal that AlexNet reaches a recognition accuracy of 65% and shows that image properties such as sharpness and contrast significantly affect performance, as measured by the mean and standard deviation. The study underlines requirements of high-quality images for applications in long-range human recognition and sheds insight into the ways enhancements in images could improve performance in deep learning models. Results support further evolution of more potent human recognition systems, particularly in application domains such as surveillance and security in which the accurate identification of a target from a long distance is highly critical. Key Words: Long-range recognition, Human recognition, Deep learning, Image processing

THE US PLACE AND ROLE AS A GLOBAL PEACEKEEPER AND PEACEBUILDER: HISTORY AND PROSPECTS IN CHANGING GLOBAL GEOPOLITICAL LANDSCAPE

The paper discusses the US place and role in global peacekeeping operations and peacebuilding processes. First, it traces the transformation of US strategic perception regarding peacekeeping and peacebuilding from 1991 to 2022. Second, on the example of Desert Storm, Kosovo, Afghanistan, Iraq, Libya, and Syria, it reveals the US approaches to the peacekeeping operations and peacebuilding processes at the practical level. Finally, the paper explores potential scenarios for US engagement in the South Caucasus and particularly, in the Armenia-Azerbaijan conflict, based on US strategic approaches and their practical implications. The paper argues that given the current political, geopolitical, and security landscape in the world and South Caucasus, the most realistic scenario may be the US direct diplomatic engagement with major support to the EU mission in Armenia.

Performance of Orthotropic Steel Plate under Combined Fire Load and Axial Compression

The orthotropic steel plate systems have been used widely in decks of long span bridges because of their large capacity and economic advantages. Early experimental and analytical research on orthotropic steel plate systems has mainly focused on their behaviors during ambient conditions. This paper presents a detailed investigation on the axial capacity of the orthotropic steel plates under fire using a sequential thermal stress analysis framework. Temperature-dependent stress-strain relationship and thermal properties of steel have been taken into consideration in the finite element modeling. Different models and parameters such as fire model, material model, geometric imperfection, residual stress and rib wall thickness have been discussed, and their effects on the axial strength of the plate have been studied and compared. Simulation results indicate that fire has significant deteriorating effects on the plate’s axial capacity. Conventional simple fire models which assume uniform surface fire loads don’t represent the real fire scenarios, and tend to overestimate the axial capacity of the plate compared that during realistic fire scenarios. Initial imperfection and residual stress in the orthotropic steel plate has negligible effect on the axial capacity of the orthotropic steel plate system under fire conditions. Increasing the thickness of rib wall could improve the fire resistance of the plate.

From Alt-text to Real Context: Revolutionizing image captioning using the potential of LLM

The "From Alt-Text to Real Context" project harnesses the transformative capability of Meta Llama-3.2-11B-Vision-Instruct, thus unleashing an unprecedented image captioning system with advanced multimodal reasoning and attention mechanisms. These descriptions are richly contextual and domain-adaptive, moving them beyond the boundaries of traditional models. The platform has been constructed very carefully, keeping a heavy robust backend in Python (Flask/FastAPI) and an interactive frontend created in React.js for easier human interaction. With strong, advanced tools that include Hugging Face Transformers, OpenCV, NumPy, and Pandas, it shines for fine-tuning models, image preprocessing, and manipulation for particular, domain-specific customizations suited for a wide variety of applications from accessibility to e-commerce and education. The system generates alt-text and can perform complex image analysis and identify key features, providing more comprehensive scene descriptions. It is equipped with sentiment analysis to decipher emotive signals; it can provide critical metrics to quantify positive sentiment. This sophisticated AI system is built on the power of LLM outshines with zero-shot and multilingual capabilities and can also be utilized in tasks like image-based captioning and storytelling. Combining state-of-the-art AI, high adaptability, and great user-friendliness, this project raises new standards for contextual understanding, applicability in real-life scenarios, and image captioning.

Effects of Real-Life Motor Complexity on Walking and Mobility in Trained and Sedentary Adolescents With Intellectual Disabilities: A Motor Dual-Task Investigation.

Adolescents with intellectual disabilities (ID) often encounter challenges in walking and mobility due to cognitive and motor impairments. This study aimed to investigate the impact of real-life motor complexity on walking and mobility in this population, particularly focusing on dual-task scenarios. Twenty-four adolescents with ID, divided into trained and sedentary groups, participated in the study. Participants completed the Timed Up and Go Test and the 10-m walk test under various conditions, including holding a water glass, carrying a tray with 2 glasses of water, introducing background noise, and encountering unexpected obstacles. Both groups significantly exhibited increased completion times (P < .001) in the Timed Up and Go Test and 10-m walk test with each added level of difficulty. The trained group demonstrated significantly superior performance (P < .05), only in the single task and holding a water glass. However, the difference between groups diminished as the challenges increased. In conclusion, real-life motor dual-tasking scenarios significantly impact walking and mobility in adolescents with ID. Physical activity may improve mobility and walking, particularly under less challenging conditions. Incorporating real-life challenges into motor dual-task training programs can enhance adaptability and reduce the risk of falls and injuries, ultimately improving the quality of life for individuals with ID.

Creating custom GPTs for faculty development: An example using the Johari Window and Crucial Conversation frameworks for providing feedback to struggling students

Feedback plays a crucial role in the growth and development of trainees, particularly when addressing areas needing improvement. However, faculty members often struggle to deliver constructive feedback, particularly when discussing underperformance. A key obstacle is the lack of comfort many faculty experience in providing feedback that fosters growth. Traditional faculty development programs designed to address these challenges can be expensive and too time-intensive, for busy clinicians.. Generative AI, specifically custom GPT models simulating virtual students and coaches, offers a promising solution for faculty development in feedback training. These AI-driven tools can simulate realistic feedback scenarios using widely accepted educational frameworks and coach faculty members on best practices in delivering constructive feedback. Through interactive, low-cost, and accessible virtual simulations, faculty members can practice in a safe environment and receive immediate, tailored coaching. This approach enhances faculty confidence and competence while reducing the logistical and financial constraints of traditional faculty development programs. By providing scalable, on-demand training, custom GPT-based simulations can be seamlessly integrated into clinical environments, fostering a supportive feedback culture prioritizing trainee development. This paper describes the stepwise process of design and implementation, of a custom GPT-powered feedback training based on an accepted framework. This process can has the potential to transform faculty development in medical education.

Unified noise estimation and denoising algorithm with filters and neural networks for denoising satellite images

ABSTRACT Recent studies have demonstrated a rising trend in the utilization of multispectral and hyperspectral image de-noising, which is crucial for improving the visual quality of images and facilitating more efficient image processing tasks. Despite significant advancements in image denoising research, removing noise without distorting the image remains a challenging task. This paper addresses these limitations of multispectral satellite images and performs denoising of multispectral images by integrating noise estimates with a hybrid denoising framework that combines the benefits of two distinct approaches to image processing. Integrating filtering with Wavelet decomposition yields a denoising solution that is more robust and accurate than either technique could achieve on its own. Experiments on multispectral images from Sentinel-2 demonstrate the applicability and generalization of the proposed method in real-life scenarios. The comparative analysis indicates that this method significantly outperforms existing techniques, thereby demonstrating its superior performance.

Predicting in vivo concentrations of dietary hop phytoestrogens by physiologically based kinetic modeling.

Hop extracts containing prenylated polyphenols such as 8-prenylnaringenin (8-PN) and its precursor isoxanthohumol (iXN) are popular among women seeking natural alternatives to hormone therapy for postmenopausal symptoms. Due to structural similarities with estrogens, these compounds act as estrogen receptor agonists. Especially 8-PN, described as the most potent phytoestrogen known to date, poses a potential risk for endocrine disruption. Therefore, its use as a hormone replacement raises concerns for human health. However, a significant challenge in assessing the potential endocrine-disruptive effects of hop polyphenols is the lack of data on their toxicokinetics. Particularly, information on in vivo concentrations in target tissues is lacking. To address this gap, we developed a physiologically based kinetic (PBK) model tailored to female physiology. The model was used to predict the levels of hop polyphenols in human blood and target tissues under realistic exposure scenarios. The predictions suggest that iXN and 8-PN concentrations in target tissues reach the low nanomolar range after dietary supplementation. This study enhances our understanding of internal concentrations of iXN and 8-PN after dietary consumption and is of direct applicability for respective risk assessment.

Application of an adaptive observer for the detection and location of pipeline leak

Leakages in hydrocarbon transport pipelines are a recurring problem. Their real-time diagnosis is a priority task. Timely resolution of leaks helps avoid human casualties, environmental harm, and infrastructure damage. This work focuses on simulating the hydrodynamic behavior along a pipeline to enable timely leak detection and localization through a novel computational tool. The proposed mathematical model identifies discrepancies between the mass and momentum equations and an adaptive observer’s estimation, which considers the leaking fluid like a system perturbation. The flow volume balance method confirms the existence of a leakage. A fluid quantifier estimates the total leaked volume during the detection period. The estimation error between the measured and estimated variables for the adaptive observer approximates the leakage location. The developed software was validated by comparing the results with a real leak scenario in a 20-inch diameter, 15 km long liquefied petroleum gas pipeline. The results demonstrate the tool’s feasibility for real-time leak detection, localization, and quantification, showcasing its potential for addressing recurring pipeline leaks.

Evaluation of the effectiveness of GASMAN anesthesia simulation software combined with case-based learning versus traditional lecture-based learning in inhalation anesthesia education.

To evaluate the effectiveness of integrating GASMAN anesthesia simulation software with case-based learning (IGC) compared to traditional lecture-based learning (LBL) in teaching inhalation anesthesia to undergraduate anesthesiology students. Fourth-year students from two academic years (2022, n = 110; 2023, n = 131) enrolled in a five-year anesthesiology program were assigned to either traditional lecture-based learning (LBL) or IGC groups. The LBL group received traditional lectures using PowerPoint slides, while the IGC group engaged with GASMAN anesthesia simulation software (a tool designed for anesthesia simulation and gas monitoring) combined with case-based learning. The cases used in the IGC group were structured around realistic clinical scenarios, simulating real-world challenges in inhalation anesthesia. These scenarios were integrated with the GASMAN software to provide interactive simulations, enhancing students' understanding of pharmacokinetics and pharmacodynamics. Teaching effectiveness was evaluated through expert assessments and student feedback, with learning outcomes compared via post-course assessments. The IGC group scored significantly higher in student evaluations in areas such as comprehending and mastering theoretical knowledge, resolving clinical challenges, nurturing clinical reasoning, increasing learning interest, enhancing learning efficiency, consolidating memory, improving analytical skills, and refining application proficiency (adjusted P < 0.001), however, there were no significant differences between the two groups in the improvement of practical skills. Post-course test scores were also higher in the IGC group for both total post-course test and subjective questions scores (adjusted P < 0.001), though no difference was found for objective question scores. After applying false discovery rate (FDR) correction, expert evaluation scores showed no significant differences between the two groups. The integration of GASMAN software with case-based learning significantly enhances the quality of inhalation anesthesia education by improving student engagement, critical thinking, and conceptual understanding. This approach demonstrates promise for advancing clinical education, although further research is needed to evaluate its long-term impact.

Enhancing Students’ Communicative Competence through Classroom Tasks in Hospitality Training

This study explores how classroom tasks enhance students’ communicative competence in an English for Receptionists training program using Project-Based Learning (PJBL). By incorporating role-playing, simulations, and task-based interactions reflecting real-life scenarios such as guest greetings, check-ins, complaint handling, and local knowledge sharing, the program aims to develop four components of communicative competence: grammatical, sociolinguistic, discourse, and strategic. A qualitative case study approach was employed, with data collected through classroom observations, interviews, and document analysis, then analyzed thematically. Findings show that tailored classroom tasks improve students’ ability to use appropriate grammar and vocabulary, adjust communication to social contexts, organize coherent responses, and employ strategies to overcome communication barriers. The study highlights the critical role of classroom tasks in bridging the gap between theoretical learning and professional demands, offering practical insights for educators to design effective ESP curricula tailored to the hospitality sector.

Attraction effect in crowded decision spaces: exploring the impact of decoys in choices among numerous options

ABSTRACT Human choices are context-dependent, and options evaluation is biased by the quality and quantity of available alternatives. In the attraction effect, dominated decoys have proven effective in shifting preferences in numerous experiments, yet its relevance in real-life choices remains disputed. Part of the problem lies in the differences between laboratory settings and realistic scenarios: in the lab, participants are tested on ternary choices; in real life, consumers face choices among many options, and interactions with other context effects are frequent. We present two experiments investigating how these factors modulate the attraction effect: we manipulate the number of decoys (study1), and the number of available options (study2). Findings suggest that: (i) the attraction effect remains significant in larger sets; (ii) two decoys are more effective than one, but (iii) adding more undermines the effect; (iv) compromise options have a dampening influence on decoys, making them ineffective at targeting the intermediate option.

Potential of role play as an educational tool in biochemistry to facilitate medical education.

Commonly used traditional didactic lecture in biochemistry being non-interactive has several disadvantages which students find boring and difficult to retain. This study reviews the potential of role play to teach biochemistry effectively. Studies published till June 2024 on the topic role play in medical education and biochemistry were searched using 'Ovid Discovery' software showing studies available in PubMed, Embase, and Cochrane databases. Studies having matched keywords like 'role play, roleplay, role-play, education, medical, biochemistry and genetics' appearing in title or text article were included while studies that were irrelevant, in non-English language or duplicated studies were excluded. Literature search revealed 8 studies for reviewing the topic. Studies that have tested effectiveness of role play in biochemistry have shown that it can bridge the gap between theory and practice. Role play is dramatization of a theme simulating real-life scenarios evoking learner's critical thinking process, activation of cognitive, psychomotor, and affective domains. It creates lasting memory in retaining topics besides motivating student for self-directed learning. It also develops confidence, communication, and language skills among students. Role play can be a powerful tool to teach biochemistry for integrating knowledge of biochemistry with clinical concepts. The authors recommend that biochemistry lectures and practical sessions should be reinforced through role plays especially for topics having clinical relevance. The author proposes several applications of role play in biochemistry to demonstrate metabolic pathways, experimental skills, metabolic disorders, accidental emergencies in lab, do's and don'ts in labs, pre analytical errors affecting biochemistry lab results.

Threats Assessment of the Endemic Idle Crayfish (Austropotamobius bihariensis Pârvulescu, 2019): Lessons From Long‐Term Monitoring

ABSTRACTThe idle crayfish (Austropotamobius bihariensis Pârvulescu, 2019), endemic to Romania's Apuseni Mountains, urgently requires a specific conservation plan. Due to its recent description, conservation efforts have been limited, highlighting the need for immediate and practical recommendations to ensure its protection. Over 13 years, field observations were conducted to evaluate population trends and identify threats following IUCN standards. Additionally, geospatial assessments and predictive modelling were employed to estimate both the optimal habitat and current population size under three distinct scenarios. The primary threats identified include poor forest management, extreme drought, anthropogenic development and riverbed alterations, all contributing to declines in crayfish abundance. The most severe impacts arise when these pressures converge at a single site, compounded by a chronic, low‐virulence crayfish plague infection (A‐haplogroup). The total population is estimated at 31,150 (± 449.9 SE) individuals, with 1,163,754 m2 of suitable habitat, of which only 37.9% lies within 13 protected areas under the most realistic scenario. Poor water quality was found to significantly reduce the modelled population size. In light of these findings, we propose a series of targeted conservation actions tailored for each protected area and highlight the importance of extending measures beyond their current boundaries. Additionally, we recommend implementing the ‘ark sites’ concept in regions with optimal ecological conditions, stable populations and genetic diversity, to reduce pressures and safeguard the species through effective field management.

Unsupervised Degradation Representation Learning for Unpaired Restoration of Images and Point Clouds.

Restoration tasks in low-level vision aim to restore high-quality (HQ) data from their low-quality (LQ) observations. To circumvents the difficulty of acquiring paired data in real scenarios, unpaired approaches that aim to restore HQ data solely on unpaired data are drawing increasing interest. Since restoration tasks are tightly coupled with the degradation model, unknown and highly diverse degradations in real scenarios make learning from unpaired data quite challenging. In this paper, we propose a degradation representation learning scheme to address this challenge. By learning to distinguish various degradations in the representation space, our degradation representations can extract implicit degradation information in an unsupervised manner. Moreover, to handle diverse degradations, we develop degradation-aware (DA) convolutions with flexible adaption to various degradations to fully exploit the degrdation information in the learned representations. Based on our degradation representations and DA convolutions, we introduce a generic framework for unpaired restoration tasks. Based on our framework, we propose UnIRnet and UnPRnet for unpaired image and point cloud restoration tasks, respectively. It is demonstrated that our degradation representation learning scheme can extract discriminative representations to obtain accurate degradation information. Experiments on unpaired image and point cloud restoration tasks show that our UnIRnet and UnPRnet achieve state-of-the-art performance.

Clairvoyant performance bounds for adaptive beamforming in pulse-echo imaging.

In adaptive beamforming, the array signal processing adjusts its sensor delays and weights based on the incoming data. In conventional beamforming, these parameters are instead given from a predefined model. Adaptive beamformers can improve measurement precision by dynamically rejecting spatial interference. While an established theory is available on the behavior of adaptive beamformers in textbook scenarios, their expected performance on realistic pulse-echo imaging scenes is still mostly uncharted. Imaging performance can be evaluated by individual pixel precision and aggregated metrics such as resolution and contrast. The achievable gain is strongly related to the sparsity of the scene and the availability of data to appropriately estimate the spatial covariance matrix. In pulse-echo measurements, the nonstationary interference poses a special problem for adaptive beamforming, which is a current research question of academic and industrial interest. The current work establishes a performance bound for adaptive beamforming in simulated realistic pulse-echo scenarios. This is derived and numerically implemented as the clairvoyant minimum variance distortionless response beamformer. The proposed framework allows for an a priori assessment of the applicability of adaptive beamforming, for a given scenario. The performance of the implemented algorithms can be directly compared with the theoretical limit in a simulated environment.