Interactive Modules Research Articles

Interactive iPad-Based Education for Parents of Patients with Cleft Lip and Palate.

To assess the effectiveness of an interactive iPad-based educational module (cleft iBook) in enhancing parent/caregiver education related to cleft lip and/or cleft palate (CL/P) care. A prospective study involving pre- and post-intervention surveys. The study was conducted at a craniofacial clinic in a dedicated children's hospital specifically during initial consultations for CL/P care. Thirty-two participants (parents/legal guardians/caregivers) ≥18 years of age, English and/or Spanish-speaking, and attending with a child with CL/P. Participants were provided with iPads and engaged with the interactive iPad-based educational module. Pre- and post-intervention surveys were administered. The survey assessed the understanding of a CL/P diagnosis and management and usability of the iBook. Survey responses were graded on a 5-point Likert scale. Total scores for pre- and post-intervention surveys were compared. Utilizing the cleft iBook module before consultation significantly enhanced comprehension in multiple domains: prenatal development of CL/P, dento-facial molding, surgical techniques, steps after the initial consultation, postoperative care, and the necessity of long-term care for affected children (P < .01). Cumulative survey scores increased by 10.2 points, reflecting significantly improved responses regarding overall comprehension (P < .001). The interactive, iPad-based cleft educational module emerges as a viable, digital strategy for providing education and empowerment to parents and caregivers navigating the challenges of caring for a child with CL/P. The cleft iBook serves as a readily accessible resource, fostering connections among the child, caregivers, and care team.

BLADE-ON-PETIOLE interacts with CYCLOIDEA to fine-tune CYCLOIDEA-mediated flower symmetry in monkeyflowers (Mimulus).

Morphological novelties, or key innovations, are instrumental to the diversification of the organisms. In plants, one such innovation is the evolution of zygomorphic flowers, which is thought to promote outcrossing and increase flower morphological diversity. We isolated three allelic mutants from two Mimulus species displaying altered floral symmetry and identified the causal gene as the ortholog of Arabidopsis BLADE-ON-PETIOLE. We found that MlBOP and MlCYC2A physically interact and this BOP-CYC interaction module is highly conserved across the angiosperms. Furthermore, MlBOP self-ubiquitinates and suppresses MlCYC2A self-activation. MlCYC2A, in turn, impedes MlBOP ubiquitination. Thus, this molecular tug-of-war between MlBOP and MlCYC2A fine-tunes the expression of MlCYC2A, contributing to the formation of bilateral symmetry in flowers, a key trait in angiosperm evolution.

Effectiveness of Game-Based Interactive Approach Using Deep Learning Framework for Dyslogia."

Traditional speech therapy approaches have long been considered revolutionary for treating speech disorders. However, as the younger generation becomes increasingly disengaged from these methods, their effectiveness is diminishing. This study identifies the need to revitalize traditional practices by integrating them into virtual environments and incorporating gamification elements. The motivation behind this work is to enhance engagement and improve therapy outcomes by making the process more appealing to children. Our proposed solution involves converting conventional speech therapy exercises into interactive virtual modules that incorporate game-like features to sustain interest and foster a competitive spirit. The method includes developing these virtual modules and testing their effectiveness through user trials. Results indicate a significant increase in engagement and a corresponding improvement in therapy outcomes, suggesting that this approach holds promise for enhancing the effectiveness of speech therapy in the digital age.

A GPU-accelerated Monte Carlo code, RT2 for coupled transport of photon, electron/positron, and neutron

Objective. This work aims to develop a graphics processing unit (GPU)-accelerated Monte Carlo code for the coupled transport of photon, electron/positron and neutron over a broad range of energies for medical applications. Approach. By separating the MC evolution of radiation into source, transport, and interaction kernels, the branch divergence was alleviated. The memory coalescence was achieved by vectorizing the access pattern in which the secondary particles were archived. To accelerate further particle tracking, ray-tracing hardware acceleration in the Nvidia OptiXTM framework was applied. For photon and electron/positron, the EGSnrc interaction modules were ported as a GPU-optimized configuration. For neutron, a group-wised transport based on NJOY21 preprocessed data was implemented. The developed code was validated against CPU-based FLUKA. Neutron, x-ray and electron beams incident on water and ICRP phantoms were simulated. The neutron energy group and the transport parameters of photon and electron were set to be the same in both codes. A single Nvidia RTX 4090 card was used in this code while all 20 threads of a single Intel Core i9-10900K node were used in FLUKA. Main results. The number of histories was set to ensure that statistical uncertainties lower than 2% for all voxels whose doses were larger than 20% of the maximum. In all cases, the dose differences in the voxels between the codes were within 2.5%. For photons and electrons, the developed code was 150–300 times faster than FLUKA in both geometries. For neutrons, the code was respectively 80 and 135 times faster in the water and ICRP phantoms than FLUKA. Significance. This study offers an appropriate solution for uncoalesced memory access and branch divergence commonly encountered in coupled MC transport on the GPU architecture. The formidable acceleration in computing times and accuracy shown in this study can promise a routine clinical use of MC simulations.

The Role of Perceived Ease of Use in the Adoption of Quizlet for Vocabulary Acquisition in Chinese EFL Contexts

This study investigates the perceived ease of use of Quizlet among Chinese college English as a Foreign Language (EFL) learners, focusing on vocabulary acquisition. Utilizing a mixed-methods approach, the research combined quantitative data from a survey (n = 95) and qualitative insights from semi-structured interviews (n = 24) following an 8-week Quizlet intervention. The quantitative results revealed high satisfaction with Quizlet, evidenced by a mean score of 3.96 on a 5-point Likert scale, highlighting its intuitive interface and multimedia features. Qualitative findings supported these results, identifying key themes such as user-friendly navigation, ease of becoming proficient and effective vocabulary learning through interactive modules. The study emphasizes the critical role of user-centered design in educational technology, suggesting that well- designed tools like Quizlet can significantly improve vocabulary acquisition and learner engagement. These insights advance the understanding of digital tool adoption in EFL contexts and offer practical recommendations for educators and developers.

SwiftWater: A lightweight SwiftFormer-based underwater image enhancement network with auxiliary prompt module

Due to variations in light attenuation underwater and the complexities of light propagation in aquatic environments, underwater images commonly suffer from degradation, such as color distortion, blur, and low contrast. These challenges pose significant obstacles to researchers studying marine resources. In recent years, numerous solutions have been proposed for underwater image enhancement (UIE) tasks, with deep learning-based methods, including those employing convolution and transformer structures, demonstrating promising performance. However, deep learning approaches often encounter issues related to large parameter sizes and substantial computational demands. Addressing these concerns, this paper introduces SwiftWater, a lightweight transformer-based network specifically designed for UIE tasks, featuring a U-Net style architecture. Leveraging the efficient SwiftFormer encoder as the network backbone, we introduce a Hybrid Squeeze and Excitation Block (HSEB) to dynamically adjust representations within skip connections. Additionally, we introduce an Auxiliary Prompt Module (APM) with a Lightweight Prompt Block (LPB) that utilizes the HSV color space of underwater images to enhance training. LPB within APM comprises two modules: the Prompt Generation Module (PGM) for prompt generation and the Lightweight Prompt Interaction Module (LPIM) for prompt-feature interaction, generating rich auxiliary image representations. Compared to existing mainstream UIE methods employing transformer structures, our proposed approach offers reduced parameter counts while achieving state-of-the-art (SOTA) performance across both quantitative and qualitative metrics.

Development of Interactive Electronic Module (E-Modul) Subject Ecosystem Based on I-Spring Application on Class X Senior High School

Technological developments and the era of digitalization in the 21st century demand innovation in education, especially in the development of learning media, which is the basis behind this research. This research aims to develop an interactive e-module with ecosystem material based on the I-Spring application for class X SMA Negeri 5 Gowa which is valid and practical. This type of research is Research and Development (R&amp;D) with the ADDIE development model which consists of five stages, namely analysis, design, development, implementation and evaluation. The research subjects were biology subject teachers and class X students at SMA Negeri 5 Gowa. Data was collected through e-module validation sheets and teacher and student response questionnaires. The validity of the emodule was measured with a validity percentage of 4.39, while practicality was measured through the responses of teachers (91.45%) and students (88.81%), both of which indicated the very practical category.Keywords: E-module, R&amp;D, ADDIE, Valid, Practical.

IMC-ResNet: Atrial fibrillation detection method based on interlayer multiscale coupling

Atrial fibrillation(AF) is one of the most common cardiac arrhythmias, and reliable detection of it is of significant importance. This study proposes a multilayer convolutional neural network for AF detection, which introduces the Multiscale Information Interaction module composed of a Detail Enhancer and a Context Information Extractor to achieve the extraction and coupling of multiscale information between layers. The Detail Enhancer is composed of multiscale transposed convolutions to encode waveform details in the electrocardiogram (ECG). The Context Information Extractor is composed of multiscale dilated convolutions to encode the morphological information of the ECG. Furthermore, the Multiscale Information Interaction module is used in conjunction with residual blocks to model the surface feature correlation and intrinsic abstract structure of AF signals. This approach extracts implicit information of AF from ECG signals, achieving high-precision and robust detection of AF. Five-fold cross-validation on the AFDB dataset resulted in an accuracy of 99.56% and an F1-score of 99.65%. Inter-patient testing on the clinical SPHDB database yielded an accuracy of 96.17% and an F1-score of 96.14%. In addition, this study conducted experiments to evaluate the model’s robustness against noise, yielding favorable results across four types of noise tests. The results indicate that the proposed method has great potential for application in clinical auxiliary diagnosis based on wearable devices.

A design thinking approach to developing an online curriculum in interprofessional education in health in South Africa

Background. The design of contemporary curricula in health professions education in South Africa (SA) is constantly evolving, as there is a need to ensure that students are adequately prepared to work in complex healthcare environments. The World Health Organization proposes that a way to accomplish this is through interprofessional education (IPE), where students from two or more professions learn from one another to improve health outcomes. Objectives. To reflect on the design of an introductory module of a postgraduate diploma in IPE in health at a historically disadvantaged institution in SA, where a student-centred approach to learning and teaching was adopted. Methods. Through an auto-ethnographic methodology, a design thinking approach was used to carefully construct and align an introductory module in IPE in the postgraduate programme. Considering the remote learning environment, this article also examines the importance of educational technologies in the design process of this online curriculum. Results. The results of this study demonstrated the process used in the design of an IPE module supported by the five stages of design thinking to critically appraise the student-centred approach to learning and teaching. The instructional design tools used for this module included the university’s learning management system, Google Suite, Flip, Google Jamboard, webinars, presentations, elevator speeches, quizzes and guest speakers. The end product was an interactive online module that was intentionally designed to be interactive and provide students with a core understanding of IPE. Conclusion. This article demonstrates how an IPE module with a focus on theories, models and concepts can provide the foundation for students to understand the importance of IPE to transform the future of healthcare in SA.

Gut Microbiota Defines Functional Direction of Colonic Regulatory T Cells with Unique TCR Repertoires.

Intestinal microbiota and selected strains of commensal bacteria influence regulatory T (Treg) cell functionality in the colon. Nevertheless, whether and how microbiota changes the transcriptome profile and TCR specificities of colonic Tregs remain to be precisely defined. In this study, we have employed single-cell RNA sequencing and comparatively analyzed colonic Tregs from specific pathogen-free and germ-free (GF) mice. We found that microbiota shifts the activation trajectory of colonic Tregs toward a distinct phenotypic subset enriched in specific pathogen-free but not in GF mice. Moreover, microbiota induced the expansion of specific Treg clonotypes with shared transcriptional specificities. The microbiota-induced subset of colonic Tregs, identified as PD-1- CXCR3+ Tregs, displayed enhanced suppressive capabilities compared with colonic Tregs derived from GF mice, enhanced production of IL-10, and were the primary regulators of enteric inflammation in dextran sodium sulfate-induced colitis. These findings identify a hitherto unknown gut microbiota and immune cell interaction module that could contribute to the development of a therapeutic modality for intestinal inflammatory diseases.

Three-phase imbalance model based on energy storage device

Abstract In order to realize the goal of carbon peaking and carbon neutrality and integration of the source network, preventing and controlling three-phase imbalance is an indispensable subject. Aiming at the above requirements, this paper proposes and develops a model that takes energy storage devices as the core, integrating with the power grid to solve the three-phase imbalance. The model includes a power module with a detector, a rectifier-inverter module, an interactive module that can switch the charging mode and state, and a regulating and balancing module with PI control. This model can not only dynamically compensate the three phases quickly and accurately, minimize the three-phase imbalance, but also realize the functions of peak shaving and peak load regulation and provide guarantee and guidance for power grid operation.

Impact of Internet Handling Skills in Swayam Portal Learning among B.Ed Trainees

SWAYAM is an acronym that stands for "Study Webs of Active-Learning for Young Aspiring Minds". MOOC Platform was developed indigenously by AICTE in 2016 to facilitate the hosting of online courses that could be accessed by anyone, anywhere at any time free of cost to achieve three cardinal principles of Education Policy viz. access, equity, and quality. On the SWAYAM portal, Internet Handling Skills would involve navigating the platform effectively, accessing course materials, participating in online discussions and forums, submitting assignments, and engaging with instructors and peers. It also includes understanding how to use the features and tools provided on the portal to enhance learning, such as multimedia content, quizzes, and interactive modules. Additionally, being aware of online etiquette and security measures while using the SWAYAM portal is essential for a positive and productive learning experience. The investigator aims to study the impact of IHS on the SWAYAM portal among B.Ed. Trainees at Pudukkottai. Pre–Test / Post–Test single group design method was applied for the present study, the investigator divided two groups namely the first group and second group both are considered to be a sample. To find out the level of handling skill in the SWAYAM Portal among B.Ed. Trainees. To study whether there is a significant difference between Pre-test and Post-test scores in handling the SWAYAM portal among the first group, second group, and total sample B.Ed. Trainees. The investigator applied an experimental research method. A purposive sampling technique was adopted. 100 B.Ed. Trainees of first-year students in Government College of Education Pudukkottai district. SWAYAM Portal-related test was used as a pre-test and post-test. The split-half reliability of the test calculated is found to be (0.855). The magnitude of the difference between the pre-test and post-test scores for handling the SWAYAM portal was estimated to be 2.8601 through Cohen’s d-effect size analysis, which was found to be very large. Thus, it was indicated that the intervention has a significant impact on Internet Handling Skills in using the SWAYAM portal. The students must possess skills in handling the internet for selection and comfortable completion of any online course, especially SWAYAM courses.

DSIL-DDI: A Domain-Invariant Substructure Interaction Learning for Generalizable Drug-Drug Interaction Prediction.

Drug-drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with unknown causal mechanisms. Deep learning methods have been developed to better understand DDI. However, learning domain-invariant representations for DDI remains a challenge. Generalizable DDI predictions are closer to reality than source domain predictions. For existing methods, it is difficult to achieve out-of-distribution (OOD) predictions. In this article, focusing on substructure interaction, we propose DSIL-DDI, a pluggable substructure interaction module that can learn domain-invariant representations of DDIs from source domain. We evaluate DSIL-DDI on three scenarios: the transductive setting (all drugs in test set appear in training set), the inductive setting (test set contains new drugs that were not present in training set), and OOD generalization setting (training set and test set belong to two different datasets). The results demonstrate that DSIL-DDI improve the generalization and interpretability of DDI prediction modeling and provides valuable insights for OOD DDI predictions. DSIL-DDI can help doctors ensuring the safety of drug administration and reducing the harm caused by drug abuse.

Effects of interactive learning module technology on Kazakh as a second language learners’ achievement and communication skills

This study aimed to investigate the effectiveness of interactive learning module technology (ILMT) in teaching Kazakh as a second language in secondary schools in the Republic of Kazakhstan. This research was carried out at Russian-medium secondary schools during the academic year 2021-2022. There were two groups in the sample: the experimental (n=63) and control (n=61). The experimental group received an education based on the ILMT while the control group received an education based on the curriculum's traditional technique. The study used a mixed research design and was divided into two sections: quantitative and qualitative. A semi-experimental pre- and post-test control group design was adopted in the quantitative section. The qualitative section employed phenomenology. The quantitative data were acquired using the students' knowledge assessment test and analyzed using comparative analysis. An interview questionnaire and observations were used to obtain qualitative data which was then evaluated using content analysis. The experimental group outperformed the control group in terms of mean post-test knowledge assessment test scores. The ILMT in teaching Kazakh as a second language is efficient in improving the quality of mastering Kazakh language by Russian-speaking students. Content analysis showed that the ILMT helps to reduce students' anxiety and increase independence which is necessary for the development of students' communication skills.

ITFuse: An interactive transformer for infrared and visible image fusion

Infrared and visible image fusion (IVIF) has attracted increasing attention from the community because of its pleasing results in downstream applications. However, most existing deep fusion models are either feature–level fusion or image–level fusion, leading to information loss. In this paper, we propose an interactive transformer for IVIF, termed ITFuse. In contrast to previous algorithms, ITFuse consists of feature interactive modules (FIMs) and a feature reconstruction module (FRM) to alternatively extract and integrate important features. Specifically, to adequately exploit the common properties of different source images, we design a residual attention block (RAB) for mutual feature representation. To aggregate the distinct characteristics that existed in the corresponding input images, we leverage interactive attention (ITA) to incorporate the complementary information for comprehensive feature preservation and interaction. In addition, cross-modal attention (CMA) and transformer block (TRB) are presented to fully merge the capitalized features and construct long-range relationships. Furthermore, we devise a pixel loss and a structural loss to train the proposed deep fusion model in an unsupervised manner for excess performance amelioration. Massive experiments on popular databases illustrate that our ITFuse performs better than other representative state-of-the-art methods in terms of both qualitative and quantitative assessments. The source code of the proposed method is available at https://github.com/tthinking/ITFuse.

Interactive Siamese spatial-Spectral cross-layer fusion transformer for hyperspectral image change detection

ABSTRACT Recently, methods based on Transformer have been widely used in the research field of hyperspectral image (HSI) change detection (CD). However, existing transformer-based CD research does not sufficiently utilize the spatial-spectral features of HSIs. In this article, we propose an interactive Siamese spatial-spectral cross-layer fusion Transformer (IS2CF-Former) network to improve the accuracy of HSI-CD. The proposed Siamese interactive module integrates the Siamese network with the Transformer structure, enhancing communication between bi-temporal images. We have made improvements to the cross-layer adaptive fusion (CAF) Transformer, where the cross-layer fusion module enhances the interaction between layers and the ability to capture local contextual features, concurrently reducing the model’s parameter count to mitigate the risk of overfitting. The CAF Transformer is applied to extract spatial and spectral features. Evaluating the detection performance of the proposed model on three bi-temporal HSIs through extensive experiments demonstrates superior accuracy compared to seven excellent CD methods.

Design and Development of Cybersecurity Suite

Protecting personal, corporate and government data in the digital age requires cybersecurity. Traditional cybersecurity methods sometimes lack comprehensive and dynamic protection mechanisms as cyber-attacks change and become more sophisticated. A new cybersecurity suite is proposed in this study. It integrates various advanced security technologies into a single user-friendly platform interaction. This paper specifically combines techniques for real-time phishing detection, strong password management, safe encryption and decryption services and an interactive module to promote user awareness and behaviours to improve digital security for individuals and organization. Rapid prototyping is used in iterative and incremental development phases to adapt to changing security needs and user input. This method allows the platform to be constantly improved to satisfy the current cybersecurity standards and react to new threats. The prototyped developed provides proactive digital defence, a complete cybersecurity platform that equips users to defend their digital presence. With the intuitive design and advanced feature set, the prototype aspires to democratize cybersecurity. Empirical testing of the prototype revealed that makes an effective protection of a wider audience without the complexities of security software. This paper contributes in the advances cybersecurity technology and shaping the digital protection measures.

A Thesis Developing Interactive E-Flipbook Module in Teaching Reading Text to the Tenth Grade Students at SMAN 4 Lubuklinggau

Reading is an important skill for students learning English, whether as a second language or a foreign language. Reading still plays a big role and is an essential tool for conveying meaning and information through text. For foreign language learners, reading in English is not an easy task because reading in English is complex and it is difficult to understand the meaning of sentences. Understanding is necessary to understand the meaning and obtain the information conveyed by the author. This happen because the language system of English is different from the students' native language. Hasibuan et al (2021) stated that Reading is an active process, it involves interpreting passages, not just receiving a message. It is reasonable for reading is one of the basic ways of acquiring information. Because when the students have a good competence in reading English, it will help them to obtain and enrich their knowledge.

An artificial intelligence platform for the screening and managing of strabismus.

Considering the escalating incidence of strabismus and its consequential jeopardy to binocular vision, there is an imperative demand for expeditious and precise screening methods. This study was to develop an artificial intelligence (AI) platform in the form of an applet that facilitates the screening and management of strabismus on any mobile device. The Visual Transformer (VIT_16_224) was developed using primary gaze photos from two datasets covering different ages. The AI model was evaluated by 5-fold cross-validation set and tested on an independent test set. The diagnostic performance of the AI model was assessed by calculating the Accuracy, Precision, Specificity, Sensitivity, F1-Score and Area Under the Curve (AUC). A total of 6194 photos with corneal light-reflection (with 2938 Exotropia, 1415 Esotropia, 739 Vertical Deviation and 1562 Orthotropy) were included. In the internal validation set, the AI model achieved an Accuracy of 0.980, Precision of 0.941, Specificity of 0.979, Sensitivity of 0.958, F1-Score of 0.951 and AUC of 0.994. In the independent test set, the AI model achieved an Accuracy of 0.967, Precision of 0.980, Specificity of 0.970, Sensitivity of 0.960, F1-Score of 0.975 and AUC of 0.993. Our study presents an advanced AI model for strabismus screening which integrates electronic archives for comprehensive patient histories. Additionally, it includes a patient-physician interaction module for streamlined communication. This innovative platform offers a complete solution for strabismus care, from screening to long-term follow-up, advancing ophthalmology through AI technology for improved patient outcomes and eye care quality.

The Design and Impact of Interactive Online Modules for Dental Faculty Calibration

The diverse backgrounds of health professions faculty often result in inconsistent teaching, clinical techniques, and feedback for students. Faculty calibration is essential for uniform, high-quality instruction. However, scheduling training sessions is challenging due to faculty availability. This study introduces a self-paced, interactive online approach to dental faculty calibration. Four self-paced online modules were developed using an interactive cloud-based platform. A variety of learning activities were interspersed throughout the module to promote active learning. A survey captured faculty’s perception of the online format. ANOVA analyses examined differences in perceived effectiveness of the online format between full-time, part-time, and adjunct faculty. The platform analytics offered insights into the faculty’s module performance. Anecdotal feedback from faculty provided evidence of behavioral changes. 94% of the faculty expressed high satisfaction with the online format. The majority of faculty agreed or strongly agreed that the online format was effective (89%), engaging (88%), and easy to navigate (84%). They highlighted the modules’ user-friendliness, flexibility, and engaging content. ANOVA analyses revealed no significant differences in perceived effectiveness of the online format between full-time, part-time, and adjunct faculty. Anecdotal feedback demonstrated that faculty were incorporating the knowledge gained from the modules into their teaching practices. This positive online experience also motivated several faculty to integrate similar online approaches into their own courses. This online approach provides a more flexible, sustainable, and interactive approach to faculty development that could be beneficial to other institutions.