Spatiotemporal analysis of contrast-enhanced ultrasound for differentiating between malignant and benign breast lesions

Objectives: The aim of this study was to apply spatiotemporal analysis of contrast-enhanced ultrasound (CEUS) loops to quantify the enhancement heterogeneity for improving the differentiation between benign and malignant breast lesions. Materials and methods: This retrospective study included 120 women (age range, 18–82 years; mean, 52 years) scheduled for ultrasound-guided biopsy. With the aid of brightness-mode images, the border of each breast lesion was delineated in the CEUS images. Based on visual evaluation and quantitative metrics, the breast lesions were categorized into four grades of different levels of contrast enhancement. Grade-1 (hyper-enhanced) and grade-2 (partly-enhanced) breast lesions were included in the analysis. Four parameters reflecting enhancement heterogeneity were estimated by spatiotemporal analysis of neighboring time-intensity curves (TICs). By setting the threshold on mean parameter, the diagnostic performance of the four parameters for differentiating benign and malignant lesions was evaluated. Results: Sixty-four of the 120 patients were categorized as grade 1 or 2 and used for estimating the four parameters. At the pixel level, mutual information and conditional entropy present significantly different values between the benign and malignant lesions (p &lt; 0.001 in patients of grade 1, p = 0.002 in patients of grade 1 or 2). For the classification of breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893 in patients of grade 1, AUC = 0.848 in patients of grade 1 or 2). Conclusions: The proposed spatiotemporal analysis for assessing the enhancement heterogeneity shows promising results to aid in the diagnosis of breast cancer by CEUS. Clinical relevance statement: The proposed spatiotemporal method can be developed as a standardized software to automatically quantify the enhancement heterogeneity of breast cancer on CEUS, possibly leading to the improved diagnostic accuracy of differentiation between benign and malignant lesions. Key Points: • Advanced spatiotemporal analysis of ultrasound contrast-enhanced loops for aiding the differentiation of malignant or benign breast lesions. • Four parameters reflecting the enhancement heterogeneity were estimated in the hyper- and partly-enhanced breast lesions by analyzing the neighboring pixel-level time-intensity curves. • For the classification of hyper-enhanced breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893).</p

Model Predictive Control for Quadcopters with Almost Global Trajectory Tracking Guarantees

This article provides a new method for trajectory tracking for quadcopters following a cascaded control approach with formal closed-loop tracking guarantees. An outer-loop model predictive controller generates twice differentiable acceleration references, which provide attitude, angular velocity, and acceleration references for a nonlinear inner-loop controller. The model predictive controller allows for tracking of references while explicitly considering that the thrust of the quadcopter is upper and lower limited. It is proven that the overall strategy renders the trajectory tracking errors uniformly almost globally asymptotically stable. Via a numerical case study, the advantages of the novel method are highlighted.</p

Effects of Anthropomorphic Design Cues of Chatbots on Users’ Perception and Visual Behaviors

Measurement of users’ perception and visual behaviors to anthropomorphic design cues of chatbots can improve our understanding of chatbots and potentially optimize chatbot design. However, as two typical and basic features, how chatbot appearances and conversational styles jointly affect users’ perception and visual behaviors remains unclear. Therefore, this study conducted an eye-tracking experiment to explore users’ perception and visual behaviors. Results indicate that anthropomorphic appearances and human-like conversational styles jointly increased users’ perception of chatbots’ social presence, trust in chatbots, and satisfaction with chatbots. In contrast, on users’ visual behaviors, such a joint effect was not found, although chatbots with higher anthropomorphic appearances and human-like conversational styles triggered more fixation counts and longer dwell time. These findings suggest that anthropomorphic appearance and human-like conversational style can improve users’ perception and attract more visual attention to chatbots. These findings provide theoretical contributions and practical implications for relevant researchers and designers.</p

Megaprojects from the lens of business and management studies:A systematic literature review

Megaprojects serve as the foundation of societal progress, providing essential infrastructure for a country's development and meeting its societal needs. There is a growing interest within the academic community to untangle the complex nature of megaprojects. This study conducts a comprehensive systematic literature review on megaprojects from the perspective of business, management, and accounting studies to provide a general map of the research conducted in this field and to highlight gaps for future research. The findings reveal thematic areas, including (i) sustainable development and decision-making, (ii) governance approach, (iii) project management, (iv) risk assessment, and (v) economic and social effects/social responsibility. Moreover, identified gaps encompass limited consideration of the use/operation and end-of-life phases, inadequate evaluation of environmental and social impacts in economic terms, insufficient focus on sustainability reporting, inclusive governance, and using novel methodologies for complex system analysis in the field of megaprojects.</p

ST-DAGCN:A Spatiotemporal Dual Adaptive Graph Convolutional Network Model for Traffic Prediction

Accurately predicting traffic flow characteristics is crucial for effective urban transportation management. Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks (GCN) have demonstrated remarkable prediction accuracy by incorporating road network topology into deep neural networks. However, many existing GCN-based models are based on the premise that the graph network is static, which may fail to do justice in replicatingthe situations in the real World. On one hand, real road networks are dynamic and undergo changes such as road maintenance and traffic control, leading to altered network structures over time. On the other hand, relationships between road sections can fluctuate due to factors like traffic accidents, weather conditions, and other events, which can significantly impact traffic patterns and result in inaccurate predictions if a static network and static relationshipsbetween nodes are assumed. To address these challenges, we propose the spatiotemporal dual adaptive graph convolutional network (ST-DAGCN) model for spatiotemporal traffic prediction, which utilizes a dual-adaptive adjacency matrix comprising both a static and a dynamic graph structure learning matrix. The dual-adaptive mechanism can adaptively learn the global features and the local dynamic features of the traffic states by updating the correlationsof nodes at each prediction step, while the gated recurrent unit (GRU), which is also a component of the model, extracts the temporal dependencies of traffic data. Through a comprehensive comparison analysis on two real-world traffic datasets, our model has achieved the highest prediction accuracy when compared to other advanced models.Accurately predicting traffic flow characteristics is crucial for effective urban transportation management. Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks (GCN) have demonstrated remarkable prediction accuracy by incorporating road network topology into deep neural networks. However, many existing GCN-based models are based on the premise that the graph network is static, which may fail to do justice in replicating the situations in the real World. On one hand, real road networks are dynamic and undergo changes such as road maintenance and traffic control, leading to altered network structures over time. On the other hand, relationships between road sections can fluctuate due to factors like traffic accidents, weather conditions, and other events, which can significantly impact traffic patterns and result in inaccurate predictions if a static network and static relationships between nodes are assumed. To address these challenges, we propose the spatiotemporal dual adaptive graph convolutional network (ST-DAGCN) model for spatiotemporal traffic prediction, which utilizes a dual-adaptive adjacency matrix comprising both a static and a dynamic graph structure learning matrix. The dual-adaptive mechanism can adaptively learn the global features and the local dynamic features of the traffic states by updating the correlations of nodes at each prediction step, while the gated recurrent unit (GRU), which is also a component of the model, extracts the temporal dependencies of traffic data. Through a comprehensive comparison analysis on two real-world traffic datasets, our model has achieved the highest prediction accuracy when compared to other advanced models.</p

Comprehensive review and state of play in the use of photovoltaics in buildings

The integration of renewable energy technologies in architecture is crucial for achieving low-carbon buildings and cities. Building-integrated photovoltaics (BIPV) represent a dynamic intersection of energy technology and sustainable construction practices. Despite the numerous available products, BIPV installations remain limited, highlighting a global need for upscaling and capacity building. This paper comprehensively analyzes BIPV technology, covering advancements, challenges, and prospects. It examines BIPV integration into architectural designs, focusing on aesthetics, design flexibility, and product diversity. Key technological breakthroughs and innovative approaches are highlighted. The review also assesses the standardization and certification of BIPV systems, emphasizing standardized practices for quality and safety. Economic feasibility is a crucial focus, with an in-depth examination of factors influencing BIPV costs. The paper synthesizes existing literature to analyze the cost-effectiveness and economic sustainability of BIPV systems through life cycle cost analyses. Additionally, it explores novel integration options offered by digital design processes. This review stands out by providing an in-depth synthesis of technological advancements, market scenarios, and regulatory environments affecting BIPV. It integrates a multidisciplinary perspective, encompassing technological, economic, and policy dimensions from applied-oriented research and industry experience. The main contributions emphasize the importance of BIPV in architectural designs, economic viability, and digital design benefits. Overall, this review is a valuable resource for understanding BIPV's role in sustainable buildings, guiding future research, and informing policymakers, practitioners, and researchers in renewable energy, architecture, and sustainable construction.</p

Guidance for goal achievement in knowledge-intensive processes using intuitionistic fuzzy sets

Throughout the execution of a knowledge-intensive process (KiP), knowledge workers need to make critical decisions such as skipping a task or canceling a process instance. These decisions significantly impact the efficiency and effectiveness of KiP execution and should, therefore, be made in a well-informed manner. When historical data, such as event logs, is available, it can be leveraged to support knowledge workers in making these decisions. However, KiPs often lack useful historical data, as each KiP instance is unique and hardly repeatable. To address this issue, this paper proposes the novel concept of potential goal achievement, i.e., the extent to which a goal can be achieved at the end of the process, considering the collected (but incomplete) data, to support knowledge workers in efficiently executing KiPs. An approach based on Intuitionistic Fuzzy Sets (IFSs) is introduced to calculate the potential goal achievement without relying on historical data. The use of potential goal achievement in supporting knowledge workers’ decisions is demonstrated, and the effectiveness of the approach is evaluated through simulations. The results demonstrate that modeling and calculating potential goal achievement support knowledge workers in achieving goals more efficiently.</p

Tailoring the properties of carbon molecular sieves membranes for the separation of propionic acid from aqueous solutions

In the fermentative production of propionic acid (PA), the major problem with batch fermentation systems is the strong inhibitory effect of PA on the production yield; one way to increase the yield is the in-situ removal of PA by using pervaporation. Acetic acid (AA) is the most important by-product in the fermentation; therefore, the membrane should be able to remove selectively PA from an aqueous solution containing AA. Considering that PA is more hydrophobic than AA and their kinetic diameter are 0.480 and 0.436 nm respectively, hydrophobic membranes with main pores in the range of around 0.5–0.6 nm with high permeation are required. Supported thin Carbon Molecular Sieve Membranes (CMSM) were prepared by the dip coating a porous alumina support into a solution containing resorcinol phenolic resin as carbon source. The hydrophobicity was obtained by carbonizing the polymer at temperatures higher than 750 °C and adding polyvinyl butyral (PVB) as pore forming agent and carbon contributor. PA with 88 % of purity was obtained by pervaporation of an aqueous solution containing 5 % of PA and 5 % of AA using a CMSM carbonized at 850 °C containing 1 % of PVB in the dipping solution.</p

Resilient Containment Under Time-Varying Networks With Relaxed Graph Robustness

This paper investigates the resilient containment control problem for leader-follower MASs in time-invariant and time-varying digraphs. Despite the existence of some noncooperative agents in the network, the cooperative followers are expected to converge to the safety interval constructed by the cooperative leaders. Specifically, to defend against malicious attacks and achieve the objective of resilient containment, each cooperative follower disregards the most suspicious values in its in-neighbor set and utilizes the retained values for state update. However, resilient containment is usually achieved at the cost of stringent graph conditions. In our work, with the introduction of a novel graph-theoretic property, namely the strongly trusted robustness, a small subset of agents is set as trusted nodes and the graph robustness requirement for resilient containment is relaxed. The constraint on the minimum number of leaders is also relaxed through this operation. Moreover, this novel property is extended to time-varying networks, for which the notion of jointly and strongly trusted robustness is proposed. This notion further relaxes the requirement that the digraph should satisfy certain graph conditions at each time step, thus reducing the communication burden. Numerical simulations are provided to validate the theoretical results.</p

A revised cognitive mapping methodology for modeling and simulation

Fuzzy Cognitive Maps (FCMs) hold promise as a mathematical tool for modeling and simulating complex systems due to their transparency, flexibility to operate on prior knowledge structures and recurrent reasoning characteristics. However, they suffer from significant shortcomings that have prevented them from being more widely used. Some of these issues include discrepancies in component interpretation, saturation of neural concepts, arbitrary nonlinearities, and dynamic behaviors that are difficult to align with the problem domain. By integrating theoretical advances with practical needs, this paper proposes a revised modeling and simulation methodology termed “neural cognitive mapping” that addresses these issues holistically. Firstly, we redefine concepts’ activation values in terms of changes rather than absolute values, ensuring a unified interpretation of the model's components. Secondly, we propose a parameterized activation function, called “exponential normalized activator”, which allows experts to control the neurons’ nonlinearities while avoiding saturation states. Furthermore, we provide a twofold reasoning rule that simultaneously computes the concepts’ changes and the amounts of resources attached to problem variables. Thirdly, we introduce a framework for interpreting simulation results across various dynamic behaviors, including scenarios with unique fixed-point attractors. The simulations using both real-world case studies and synthetically generated data illustrate the superiority of our proposal compared with the traditional approach in terms of clarity, usefulness, consistency, and controllability. Moreover, the empirical studies opened new research directions to be explored in future research.</p