Interactive Framework Research Articles

Dynamin1 long- and short-tail isoforms exploit distinct recruitment and spatial patterns to form endocytic nanoclusters

Endocytosis requires a coordinated framework of molecular interactions that ultimately lead to the fission of nascent endocytic structures. How cytosolic proteins such as dynamin concentrate at discrete sites that are sparsely distributed across the plasma membrane remains poorly understood. Two dynamin-1 major splice variants differ by the length of their C-terminal proline-rich region (short-tail and long-tail). Using sptPALM in PC12 cells, neurons and MEF cells, we demonstrate that short-tail dynamin-1 isoforms ab and bb display an activity-dependent recruitment to the membrane, promptly followed by their concentration into nanoclusters. These nanoclusters are sensitive to both Calcineurin and dynamin GTPase inhibitors, and are larger, denser, and more numerous than that of long-tail isoform aa. Spatiotemporal modelling confirms that dynamin-1 isoforms perform distinct search patterns and undergo dimensional reduction to generate endocytic nanoclusters, with short-tail isoforms more robustly exploiting lateral trapping in the generation of nanoclusters compared to the long-tail isoform.

School social workers’ attitudes to inclusion in regular classrooms in the United Arab Emirates

ABSTRACT The aim of this research is to investigate school social workers’ attitudes to educational inclusion in regular classrooms regarding different variables. Using an attitude scale, we investigated 232 social workers from the UAE regarding age, gender, nationality, type of school, years of experience, and area of residence. Educational placement variables for different disabilities, and school accessibility, were also investigated. Gender, age, school type, and experience did not impact attitudes to inclusion, but nationality, residence, and educational placement did. Social workers reported that UAE schools are inaccessible to students with disabilities. Inclusive education provides better quality education for all and helps change discriminatory attitudes. Schools enable the development of social ties and interactions through a framework for first interactions outside the home. When students with different skills and experiences play and learn together, respect and understanding increase. School social workers can be the key to facilitating this.

African American style project pitches in business fundraising: Strategies for African American entrepreneurs

African American restaurateurs’ number one challenge is limited access to capital, and crowdfunding can be an alternative financing vehicle. However, funding success of African American restaurateurs is significantly lower than that of Caucasian counterparts. To address the gap, the current study examined whether the linguistic styles of African American restaurateurs impact their crowdfunding success. Based on the symbolic interaction framework and 2907 restaurant crowdfunding projects from Kickstarter.com, we found that African American restaurateurs’ project descriptions show distinctive linguistic styles. African Americans use a considerably fewer articles (i.e., “a, “an,” and “the”) and a substantially higher number of religious terms than their Caucasian counterparts. The usage of religious words and negations (e.g., the usage of “none” or “never” in place of “any” or “ever”) are also found to affect fundraising success negatively. This study’s findings help African Americans to compose more effective project narratives to enhance their ability to obtain financing.

Reinforcement Learning for Closed-loop Regulation of Cardiovascular System with Vagus Nerve Stimulation: A Computational Study.

(Objective). Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study. (Approach). Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate (HR) and the mean arterial pressure (MAP) for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control (PILCO). (Main results). Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of Deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems. (Significance). We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

Characterizing the Behavior of Water Interacting With a Nano-Pore Material: A Structural Investigation in Native Environment Using Magnetic Resonance Approaches.

The study of fluid absorption, particularly that of water, into nanoporous materials has garnered increasing attention in the last decades across a broad range of disciplines. However, most investigation approaches to probe such behaviors are limited by characterization conditions. In this study, a combined MRI and MAS NMR method was used to study a nanoporous silica glass to acquire information about its structural framework and interactions with confined water. Specifically, MRI was used for a quantitative analysis of water extent. While MAS NMR techniques provided structural information of silicate materials, including interactive surface area and framework packing. Analysis of water spin-spin relaxation times (T2) suggested differences in water confinement within the characterized framework. Subsequent unsuccessful delivery of paramagnetic molecule into the pores enabled a quantitative assessment of the dimensions that "bottleneck" the pores. Finally, pore sizes were derived from the molecular size, density function theory (DFT) simulation and characterizations on standard samples. Our result matches with Brunauer-Emmett-Teller (BET) analysis.. The use of a paramagnetic probe for pore size determination introduces a new approach of characterization in the liquid phase, offering an alternative to the conventional BET analysis that uses gas molecule as probes.

Interactive attack-defense for generalized person re-identification

Generalized Person Re-Identification (GReID) aims to develop a model capable of robust generalization across unseen target domains, even with training on a limited set of observed domains. Recently, methods based on the Attack-Defense mechanism are emerging as a prevailing technology to this issue, which treats domain transformation as a type of attack and enhances the model’s generalization performance on the target domain by equipping it with a defense module. However, a significant limitation of most existing approaches is their inability to effectively model complex domain transformations, largely due to the separation of attack and defense components. To overcome this limitation, we introduce an innovative Interactive Attack-Defense (IAD) mechanism for GReID. The core of IAD is the interactive learning of two models: an attack model and a defense model. The attack model dynamically generates directional attack information responsive to the current state of the defense model, while the defense model is designed to derive generalizable representations by utilizing a variety of attack samples. The training approach involves a dual process: for the attack model, the aim is to increase the challenge for the defense model in countering the attack; conversely, for the defense model, the focus is on minimizing the effects instigated by the attack model. This interactive framework allows for mutual learning between attack and defense, creating a synergistic learning environment. Our diverse experiments across datasets confirm IAD’s effectiveness, consistently surpassing current state-of-the-art methods, and using MSMT17 as the target domain in different protocols resulted in a notable 13.4% improvement in GReID task average Rank-1 accuracy. Code is available at: https://github.com/lhf12278/IAD.

Deep-learning-based prediction framework for protein-peptide interactions with structure generation pipeline

Deep-learning-based prediction framework for protein-peptide interactions with structure generation pipeline

Multi-AUV Heterogeneous Bipartite Consensus Formation Obstacle Avoidance Algorithm Based on Event Triggering-RMPC Under Measurement-Communication Union Framework

This paper investigates a robust model predictive control-based event triggering (ET-RMPC) for heterogeneous autonomous underwater vehicle (AUV) bipartite consensus formation in obstacle avoidance. Firstly, a scheme of heterogeneous AUV bipartite formation control base on signed graph theory and implicit interactive measure-communication union framework is proposed. Next, a RMPC based on the proposed framework for heterogeneous AUV bipartite consensus formation is designed. Additionally, to alleviate the computational burden, a novel event-triggering mechanism is devised based on the error between the estimated state and actual state of the AUV formation trajectory. Then, the stability of the system and the upper and lower bounds of the trigger interval are analyzed based on innovation and Lyapunov theory. Finally, the effectiveness of the proposed method is validated through bipartite consensus formation tracking and obstacle avoidance scenarios.

Talk-to-Edit: Fine-Grained 2D and 3D Facial Editing via Dialog.

Facial editing is to manipulate the facial attributes of a given face image. Nowadays, with the development of generative models, users can easily generate 2D and 3D facial images with high fidelity and 3D-aware consistency. However, existing works are incapable of delivering a continuous and fine-grained editing mode (e.g., editing a slightly smiling face to a big laughing one) with natural interactions with users. In this work, we propose Talk-to-Edit, an interactive facial editing framework that performs fine-grained attribute manipulation through dialog between the user and the system. Our key insight is to model a continual "semantic field" in the GAN latent space. 1) Unlike previous works that regard the editing as traversing straight lines in the latent space, here the fine-grained editing is formulated as finding a curving trajectory that respects fine-grained attribute landscape on the semantic field. 2) The curvature at each step is location-specific and determined by the input image as well as the users' language requests. 3) To engage the users in a meaningful dialog, our system generates language feedback by considering both the user request and the current state of the semantic field. We demonstrate the effectiveness of our proposed framework on both 2D and 3D-aware generative models. We term the semantic field for the 3D-aware models as "tri-plane" flow, as it corresponds to the changes not only in the color space but also in the density space. We also contribute CelebA-Dialog, a visual-language facial editing dataset to facilitate large-scale study. Specifically, each image has manually annotated fine-grained attribute annotations as well as template-based textual descriptions in natural language. Extensive quantitative and qualitative experiments demonstrate the superiority of our framework in terms of 1) the smoothness of fine-grained editing, 2) the identity/attribute preservation, and 3) the visual photorealism and dialog fluency. Notably, the user study validates that our overall system is consistently favored by around 80% of the participants.

A coupled high-accuracy phase-field fluid–structure interaction framework for Stokes fluid-filled fracture surrounded by an elastic medium

In this work, we couple a high-accuracy phase-field fracture reconstruction approach iteratively to fluid–structure interaction. The key motivation is to utilize phase-field modelling to compute the fracture path. A mesh reconstruction allows a switch from interface-capturing to interface-tracking in which the coupling conditions can be realized in a highly accurate fashion. Consequently, inside the fracture, a Stokes flow can be modelled that is coupled to the surrounding elastic medium. A fully coupled approach is obtained by iterating between the phase-field and the fluid–structure interaction model. The resulting algorithm is demonstrated for several numerical examples of quasi-static brittle fractures. We consider both stationary and quasi-stationary problems. In the latter, the dynamics arise through an incrementally increasing given pressure.

Whole-heart modelling with valves in a fluid–structure interaction framework

Whole-heart modelling with valves in a fluid–structure interaction framework

Standardization in the health care system and interaction of participants during therapeutical and diagnostic process (literature review)

Standardization in the health care is necessary at all levels, in the field of structure, process, results, which positively affects the quality of medical care. Today, the issues of interaction between participants in the treatment and diagnostic process, both at the level of a medical institution and at the level of the health care system as a whole, have not been fully considered. The purpose of the study: based on the data of current reports and regulatory legal acts on standardization in the health care system, to scientifically substantiate the need to propose mechanisms for improving this process within the framework of effective interaction between participants in the treatment and diagnostic process. This manuscript presents data from scientific publications of such databases as Web of Science, Scopus, The Cochrane Library, MedLine, CyberLeninka, RSCI, etc., as well as official documents related to the regulatory framework governing the standardization process in health care system. As a result of the analysis of scientific publications, official documents of legal regulation; procedures and standards for the provision of medical care; clinical guidelines; medical and economic standards regarding various aspects of standardization in the health care system, data were obtained regarding the current state of the standardization process in health care; provisions were formulated on the need for improvement in this area within the framework of the interaction of participants in the diagnostic and treatment process. The main features of standardization in health care are analyzed and there is a scientific justification for the need to introduce measures to improve it. The development of the most optimal standardization mechanisms in the health care system in the future can significantly improve the quality of management in the health care system and social, medical, economic efficiency.

A framework for visual-based adaptive object-robot interaction of a mobile service robot

The COVID-19 pandemic (hereinafter “the pandemic”) necessitated social distancing measures and limited physical contact, prompting the exploration of alternative methods for tasks like object delivery. Mobile service robots emerged as a potential solution, offering a bridge between humans and various tasks. While existing techniques have been introduced to enable robots to deliver objects in an end-to-end manner, they come with limitations. Grippers, for instance, can deliver only one object per round, cabinet robots require manual speed tuning to keep the object in place, and object holders lack generalizability. Inspired by the idea of human nature to use a tray to deliver the object, we developed the Visual-Based Adaptive Interaction System (hereinafter “VAIS”), a novel learning system, to improve service delivery using visual information and a fast neural learning mechanism. Within this system, the robot learns the optimal angular rotational and linear translational moving speeds to effectively transport objects placed on a tray without an extra holder. The robot validates these learnt movements by successfully completing multiple-object delivery tasks along designated routes. The results exhibit that the robot can utilize online learning after a few attempts to determine its proper moving speed and deliver different objects to a given location.

Value formation in AI-integrated service system: review and implications on hospitality and tourism research

ABSTRACT The ongoing Fourth Industrial Revolution, which is driven by artificial intelligence (AI) and other disruptive technologies, has attracted a surge of AI studies. Previous reviews primarily offered a bird’s view of AI research and were somewhat one-sided focusing on AI’s bright sides. Reviews on the networks of resources, process dynamics, and divergent outcomes of this complex system are rare, if any. This review article aims to fill this research gap by deconstructing how value is formed in the AI-integrated service system. Guided by the SPAR-4-SLR protocol and interactive value formation framework, this study reviewed and analyzed state-of-the-art AI studies (i.e. 2019–2023) published in six selected top-tier hospitality and tourism journals. This review makes contributions by outlining the resource orchestration, resource integration practices, and divergent outcomes of value formation in the AI-integrated system in hospitality and tourism. Future research directions and implications were also provided to advance the field.

Photon-Added Deformed Peremolov Coherent States and Quantum Entanglement

In the present article, we build the excitedcoherent states associated with deformed su(1,1) algebra (DSUA), called photon-added deformed Perelomov coherent states (PA-DPCSs). The constructed coherent states are obtained by using an alterationof the Holstein–Primakoff realization (HPR) for DSUA. A general method to resolve of the problem of the unitary operator was developed for these kinds of quantum states. The Mandel parameter is considered to examine the statistical properties of PA-DPCSs. Furthermore, we offer a physical method to generate the PA-DPCSs in the framework of interaction among fields and atoms. Finally, we introduce the concept of entangled states for PA-DPCSs and examine the entanglement properties for entangled PA-DPCSs.

A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learning

This paper presents an innovative strategy that utilizes reinforcement learning to enhance the fast balance charging of lithium-ion battery packs. We develop an interactive framework for lithium-ion batteries by utilizing an electro-thermal coupled model that incorporates hysteresis and temperature impacts. This framework provides precise simulation within a user-friendly gym environment. We propose a novel priority-objective reward function to address the joint challenge of battery pack balancing and fast charging. This reward function is then integrated into the Soft Actor–Critic (SAC) algorithm, delivering an optimal solution within a unified problem framework for the first time. Through a series of comprehensive simulations, we validate the effectiveness of our approach in terms of both performance and computational costs. Our method consistently achieves accelerated charging times, maintains a uniform cell state of charge distribution, and minimizes safety hazards.

A variance-based optimization for determining ground and excited N-electron wave functions within the doubly occupied configuration interaction scheme.

This work describes optimizations of N-electron system wave functions by means of the simulated annealing technique within the doubly occupied configuration interaction framework. Using that technique, we minimize the energy variance of a Hamiltonian, providing determinations of wave functions corresponding to ground or excited states in an identical manner. The procedure that allows us to determine electronic spectra can be performed using treatments of restricted or unrestricted types. The results found in selected systems, described in terms of energy, spin, and wave function, are analyzed, showing the performance of each method. We also compare these results with those arising from more traditional approaches that minimize the energy, in both restricted and unrestricted versions, and with those obtained from the full configuration interaction treatment.

Revolutionizing Higher Education Teaching Evaluation with Interactive Mobile and Mixed Reality: A Methodological and Applied Analysis

In higher education, it is imperative to maintain educational quality, which requires necessitates a thorough evaluation of teaching methods. The advent of mixed reality (MR) technology, combined with interactive mobile capabilities, introduces innovative possibilities for traditional educational assessment frameworks. This study is dedicated to investigating the application of MR and interactive mobile technologies in higher education teaching evaluations, with a focus on assessing their effectiveness and implementation outcomes. Research to date has explored MR’s educational applications. However, the integration of interactive mobile technologies alongside MR in developing specific methodologies and evaluative tools for teaching evaluations has not been fully realized. This underutilizes the combined potential of these technologies. This paper is anchored in two principal research endeavors. Initially, it delves into the construction of a meta-model facilitated by MR-aided and interactive mobile-enhanced teacher-student interactions, utilizing a hierarchical remote interaction pyramid model. This process involves developing extensible associative functions, determining index weights, and establishing evaluation levels, which collectively enhance the evaluation’s multidimensionality, interactivity, and scientific precision. Subsequently, the analytic hierarchy process (AHP) is employed to quantitatively assess the educational impact of MR and interactive mobile experiences on students. This approach provides support for customized and precise teaching evaluations. The findings reveal that integrating MR technology with interactive mobile capabilities significantly enhances the interactivity and systematic scientific approach of teaching evaluations. This fosters a more multidimensional and interactive framework for educational evaluation.

The Role of Cloud Radiative Effects in the Propagating Southern Annular Mode

AbstractThe Southern Annular Mode (SAM) is the most dominant natural mode of variability in the mid‐latitudes of the Southern Hemisphere (SH). However, both the sign and magnitude of the feedbacks from the diabatic processes, especially those associated with clouds, onto the SAM remain elusive. By applying the cloud locking technique to the Energy Exascale Earth System Model (E3SM) atmosphere model, this study isolates the positive feedback from the cloud radiative effect (CRE) to the SAM. Feedback analysis based on a wave activity‐zonal momentum interaction framework corroborates this weak but positive feedback. While the magnitude of the CRE feedback appears to be secondary compared to the feedbacks from the dry and other diabatic processes, the indirect CRE effects through the interaction with other dynamical and thermodynamical processes appear to play as important a role as the direct CRE in the life cycle of the SAM. The cross‐EOF analysis further reveals the obstructive effect of the interactive CRE on the propagation mode of the SH zonal wind directly through the CRE wave source and/or indirectly through modulating other diabatic processes. As a result, the propagation mode becomes more persistent and the SAM it represents becomes more predictable when the interactive CRE is disabled by cloud locking. Future efforts on inter‐model comparisons of CRE‐denial experiments are important to build consensus on the dynamical feedback of CRE.

An interactive method for bridging the gap between deep learning based building contour segmentation and manual annotation

ABSTRACT Although the emergence of deep learning has improved the performance of automatic building extraction, there is still a long way to go before it can completely replace the labour-intensive manual delineation of building contours. To narrow the gap between building extraction results of deep learning methods and manual-level annotations, this paper introduces an interactive semantic segmentation framework that uses manual clicks as interactive information to guide the process of semantic segmentation towards the manual annotation level. In our framework, we first use an interactive semantic segmentation network for coarse building extraction from high resolution remote sensing images. Then, we use an optimization network to further refine the extraction results. We comprehensively compare the automatic deep learning methods, the proposed interactive building extraction framework, and the full manual delineation in practical experimental settings. First, our interactive method can significantly improve the performance of automatic building extraction. Second, by comparing the efficiency of manual annotation using the ArcGIS software and our interactive method, it is found that the proposed method can save more than half of the time. The above results show the potential of the interactive method in improving the efficiency of contour annotation and reducing the cost of manual annotation.