Effectiveness Of Module Research Articles

Effects of Prolonged Exposure Therapy and Compassion-Focused Therapy on Interpersonal-Related Posttraumatic Stress Disorder in a Latinx Population: An Experimental Single-Case Study

ABSTRACT Prolonged Exposure (PE) and Compassion-Focused Therapy (CFT) are two different treatments for PTSD, however, there is limited research on their combined efficacy, particularly in populations with high trauma-related shame and guilt. The present study aimed to evaluate the effectiveness of combining PE and CFT in Latinx individuals with interpersonal trauma controlling for order effects and treatment differences during phases. An experimental withdrawal crossover single-case design was conducted with four participants reporting PTSD following interpersonal trauma and randomized into one of the two conditions: Condition 1 (A/C/B/C/B) and Condition 2 (A/B/C/B/C). Each condition began with baseline sessions (A) for active listening and trauma assessment, followed by alternating interventions of Prolonged Exposure (PE) (B) and Compassion-Focused Therapy (CFT) (C). Both conditions were found to be effective, with all participants showing significant and reliable improvements in PTSD symptoms at follow-up. The combined approach demonstrated improvements in PTSD severity and associated emotional experiences, particularly in reducing shame. However, variability in the effectiveness of the CFT modules was noted, with later phases showing more substantial benefits. The integration of PE and CFT appears to offer a promising treatment strategy for PTSD, especially for individuals with high levels of trauma-related shame. This study highlights the potential of combining exposure-based and compassion-focused interventions to enhance treatment outcomes. Future research might explore shame, guilt, experiential avoidance, and participant social identities in the context of interpersonal-related trauma.

Video Description Generation Method Based on Contrastive Language–Image Pre-Training Combined Retrieval-Augmented and Multi-Scale Semantic Guidance

In view of the insufficiency of the text encoder using CLIP and the insufficiency of the interaction between the two towers using CLIP, a CLIP-based video description model RAMSG is proposed, which combines retrieval augmentation with multi-scale semantic guidance. Firstly, RAMSG uses the visual and text encoder module of CLIP to achieve cross-modal retrieval and extract relevant text as a supervisory signal. Then, the intrinsic order of semantic words is obtained by a semantic detector and ranker. Finally, the global and local semantic guidance of the multi-scale semantic guidance module is used to improve the video description generation effect of the decoder module. Experimental results on the video description datasets MSR-VTT and VATEX show that the RAMSG model has a significant improvement in several performance indicators compared with other work, and the additional text semantics obtained through videotext matching tasks greatly improve the performance of the model.

Improving vascular access knowledge and assessment skill of hemodialysis staff.

Vascular access malfunction and failure contribute to morbidity and hospitalization in hemodialysis populations. Although controversy still exists over the identification and appropriate management of access malfunction, recognition of sentinel signs during physical examination remains an efficient way to screen for access malfunction. Dialysis staff are on the front line of providing quality care to dialysis patients, often being the first ones who could detect early physical signs of access malfunction. The study's purpose is to determine the effect of an advanced vascular access educational module presented to hemodialysis nurses and technicians, focusing on physical examination findings to identify a dialysis access at risk for malfunction. Utilizing a quasi-experimental pretest and posttest group design with a nonequivalent comparison control group, the effect of an advanced vascular access education module to improve vascular access knowledge and skill in recognition of sentinel signs of access malfunction was studied in a group of hemodialysis nurses (registered nurses [RNs]) and certified patient care technicians (PCTs). Knowledge post-test scores (RN, M=94.44, SD=7.05; PCT, M=90.83, SD=7.93) were significantly higher than pretest scores (RN, M=79.54, SD=12.47; PCT M=80.67, SD=7.99) in the intervention group (p<0.001) but not in the comparison group. There were no statistically significant differences in mean skill scores between dialysis nurses (p=0.38) and PCTs (p=0.826) or between intervention and comparison groups (p=0.332). This study exposes a critical gap in the transition of vascular access knowledge to the practical skill of access assessment. The findings suggest the need for restructuring the clinical training of dialysis nurses and PCTs in vascular access management and care. Newer active learning educational strategies in physical assessment of hemodialysis vascular access should be explored to further support dialysis nurses and PCTs in providing optimal patient care.

Data-Oriented Octree Inverse Hierarchical Order Aggregation Hybrid Transformer-CNN for 3D Medical Segmentation.

The hybrid CNN-transformer structures harness the global contextualization of transformers with the local feature acuity of CNNs, propelling medical image segmentation to the next level. However, the majority of research has focused on the design and composition of hybrid structures, neglecting the data structure, which enhance segmentation performance, optimize resource efficiency, and bolster model generalization and interpretability. In this work, we propose a data-oriented octree inverse hierarchical order aggregation hybrid transformer-CNN (nnU-OctTN), which focuses on delving deeply into the data itself to identify and harness potential. The nnU-OctTN employs the U-Net as a foundational framework, with the node aggregation transformer serving as the encoder. Data features are stored within an octree data structure with each node computed autonomously yet interconnected through a block-to-block local information exchange mechanism. Oriented towards multi-resolution feature data map learning, a cross-fusion module has been designed that associates the encoder and decoder in a staggered vertical and horizontal approach. Inspired by nnUNet, our framework automatically adapts network parameters to the dataset instead of using pre-trained weights for initialization. The nnU-OctTN method was evaluated on the BTCV, ACDC, and BraTS datasets and achieved excellent performance with dice score coefficient (DSC) 86.95, 92.82, and 90.61, respectively, demonstrating its generalizability and effectiveness. Cross-fusion module effectiveness and model scalability are validated through ablation experiments on BTCV and Kidney. Extensive qualitative and quantitative experimental results demonstrate that nnU-OctTN achieves high-quality 3D medical segmentation that has competitive performance against current state-of-the-art methods, providing a promising idea for clinical applications.

Validitas Modul Ajar Pada Materi Adab Bersosial Media dalam Pandangan Islam di Kelas 8 MTS Terpadu Berkah

The rapid development of digital technology demands character education as well as ethical guidelines for interacting in social media, especially social media manners in Islamic views at Berkah Integrated MTs Palangkaraya. This study aims to assess the validity of teaching modules on adab social media in the view of Islam for class VIII students of MTs Terpadu Berkah. The research method used is Research and Development (R&amp;D) with the ADDIE model at the analysis and design stages. Validation was carried out by four experts, namely 2 material experts, and 2 design experts. The assessment included six aspects: content suitability, alignment with the curriculum, relevance to students, language, layout and design, and learning evaluation. Data were analyzed using a Likert scale to calculate the percentage of validity. The results showed that the teaching module had an average validity of 80%, which included the valid category. This shows that the module is feasible to use in learning. However, there are some minor revisions that need to be made, such as improving visual design, images, fonts, and simplifying language, and adding learning activities that are more interactive and fun. In conclusion, this teaching module can be used as a teacher's guide in learning that helps students understand and apply social media manners in accordance with Islamic values. Additional revisions from validators are expected to increase the effectiveness of the teaching module for students

Exploring the effectiveness of a multimodal self-regulated learning grammar module for middle school students

Exploring the effectiveness of a multimodal self-regulated learning grammar module for middle school students

GRL-PUL: predicting microbe-drug association based on graph representation learning and positive unlabeled learning.

Extensive research has confirmed the widespread presence of microorganisms in the human body and their crucial impact on human health, with drugs being an effective method of regulation. Hence it is essential to identify potential microbe-drug associations (MDAs). Owing to the limitations of wet experiments, such as high costs and long durations, computational methods for binary classification tasks have become valuable alternatives for traditional experimental approaches. Since validated negative MDAs are absent in existing datasets, most methods randomly sample negatives from unlabeled data, which evidently leads to false negative issues. In this manuscript, we propose a novel model based on graph representation learning and positive-unlabeled learning (GRL-PUL), to infer potential MDAs. Firstly, we screen reliable negative samples by applying weighted matrix factorization and the PU-bagging strategy on the known microbe-drug bipartite network. Then, we combine muti-model attributes and constructed a microbe-drug heterogeneous network. After that, graph attention auto-encoder module, an encoder combining graph convolutional networks and graph attention networks, is introduced to extract informative embeddings based on the microbe-drug heterogeneous network. Lastly, we adopt a modified random forest as the final classifier. Comparison experiments with five baseline models on three benchmark datasets show that our model surpasses other methods in terms of the AUC, AUPR, ACC, F1-score and MCC. Moreover, several case studies show that GRL-PUL could capably predict latent MDAs. Notably, we further verify the effectiveness of a reliable negative sample selection module by migrating it to other state-of-the-art models, and the experimental results demonstrate its ability to substantially improve their prediction performance.

Path planning and engineering problems of 3D UAV based on adaptive coati optimization algorithm

In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration–exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, to prevent entrapment in local optima, ACOA introduces an Adaptive Levy Flight strategy to maintain population diversity, thereby improving convergence accuracy. Furthermore, underperforming individuals are eliminated using a cosine disturbance-based differential evolution strategy to enhance the overall quality of the population. The efficacy of ACOA is assessed across four dimensions: population diversity, exploration–exploitation balance, convergence characteristics, and diverse strategy variations. Ablation experiments further validate the effectiveness of individual strategy modules. Experimental results on CEC-2017 and CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance of ACOA compared to COA and other state-of-the-art optimization algorithms. Finally, ACOA’s applicability and superiority are reaffirmed through experimentation on five real-world engineering challenges and a complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.

Development of an Integrated Teaching Module for TaRL Approach Based on Differentiated Learning

This research focuses on the issue of students failing to achieve learning goals due to not learning at their ability level. Educators must have pedagogical competence, including the ability to develop good and creative learning tools to create an active and interesting learning atmosphere. The TaRL approach, developed in elementary schools using differentiated learning, The TaRL approach is designed in line with student achievement, abilities, and needs. Developing a differentiation-based TaRL approach will create an active learning atmosphere and provide learning opportunities according to student needs. Educators must realize that each student is unique and has different qualities from birth. Of course, we need to fulfill their educational needs as best as possible to adapt education to student's needs and skill levels, ensuring they meet the required learning outcomes. The research uses mixed methods, including research and development, validity, and practicality. Data was collected through practice angle and validity instruments, with quantitative and qualitative data used to assess the effectiveness of TaRL modules. The high validity category of product validation showed a high validation rate of 92%, with 96% media validity, 97.16% material validation, and 89.43% language validation. The study also included teachers' and students' opinions on the differentiation-based efficacy of TaRL modules.

Inclusive collaboration via class preparation

This paper explores the implementation and effectiveness of pre-class preparation modules in a large-scale linear algebra course for engineering students, aimed at promoting inclusion, and active participation in an active learning environment. The study investigates the impact of tailored pre-class readings and post-class materials on student engagement, preparedness, and overall performance. Through survey data collected during the fall 2023 semester, we analyze students' preferences for pre-class content delivery methods, their perceived preparedness for class, and the correlation between class preparedness and final exam grades. Our findings suggest that high-quality pre-class content positively contributes to the students experience in an active learning classrooms, with potential implications for mitigating hierarchical structures and addressing disparities in student engagement and achievement.

Raman Spectral Feature Enhancement Framework for Complex Multiclassification Tasks.

Raman spectroscopy enables label-free clinical diagnosis in a single step. However, identifying an individual carrying a specific disease from people with a multi-disease background is challenging. To address this, we developed a Raman spectral implicit feature augmentation with a Raman Intersection, Union, and Subtraction augmentation strategy (RIUS). RIUS expands the data set without requiring additional labeled data by leveraging set operations at the feature level, significantly enhancing model performance across various applications. On a challenging 30-class bacterial classification task, RIUS demonstrated a substantial improvement, increasing the accuracy of ResNet by 2.1% and that of SE-ResNet by 1.4%, achieving accuracies of 85.7% and 87.1%, respectively, on the Bacteria-ID-4 Data set, where RIUS improved ResNet and SE-ResNet accuracies by 13.6% and 14.5%, respectively, with only ten samples per category. When the sample size was reduced, accuracy gains increased to 31.7% and 38.3%, demonstrating the method's robustness across different sample volumes. Compared to basic augmentation, our method exhibited superior performance across various sample volumes and demonstrated exceptional adaptability to different levels of complexity. RIUS exhibited superior performance, particularly in complex settings. Moreover, cluster analysis validated the effectiveness of the implicit feature augmentation module and the consistency between theoretical design and experimental results. We further validated our approach using clinical serum samples from 70 breast cancer patients and 70 controls, achieving an AUC of 0.94 and a sensitivity of 92.9%. Our approach enhances the potential for precisely identifying diseases in complex settings and offers plug-and-play enhancement for existing classification models.

Evaluating the Effectiveness of a Project-Based Learning Module (McodE) in Solar Still Projects

Evaluating the Effectiveness of a Project-Based Learning Module (McodE) in Solar Still Projects

Transforming Technical Education: The Effect of the AR-MicroC Augmented Reality Learning Module on Students Achievement

Technological advances driven by Industrial Revolution 4.0 (IR4.0) have introduced digital technologies such as Augmented Reality (AR) that are changing the approach to learning. This study aims to evaluate the effectiveness of using the AR-MicroC Augmented Reality Learning Module in improving student achievement in the Information Technology Certificate program at the Malaysian Community College. A quantitative approach with a quasi-experimental design was used, involving 66 students who were divided into two groups: a treatment group (n=32) who used the AR-MicroC module and a control group (n=34) who followed conventional teaching methods. Data was collected through pre-test and post-test for the Introduction to Internet of Things (IoT) course. The study lasted for five weeks, involving pre-test, teaching, and post-test phases. Data analysis using independent sample t-test and paired sample t-test showed a significant difference in achievement between the two groups. The results of the study prove that the AR-MicroC module is effective in improving student performance. The implications of this study suggest the use of the AR-MicroC Augmented Reality Learning Module as an innovative alternative teaching tool in the learning process, especially in transforming technical education.

IMPACT OF THE ARIS MODULE (ANXIETY AND DEPRESSION REDUCTION THROUGH ISLAMIC SPIRITUAL CARE) ON PATIENTS WITH CORONARY HEART DISEASE

Background: Coronary heart disease (CHD) is a significant health issue that causes both physical and psychological distress, including heightened anxiety and depression. Islamic-based spiritual care practices may improve mental well-being in CHD patients. Objectives: This study aims to evaluate the effectiveness of the ARIS Module (Anxiety and Depression Reduction through Islamic Spiritual Care) in reducing anxiety and depression among CHD patients. Methods: Conducted from September to October 2024 at Siti Khadijah Islamic Hospital in Palembang, the study included 84 CHD patients, divided into an intervention group (42) and a control group (42), selected through purposive sampling. A pre- and post-intervention design was used to assess anxiety and depression levels before and after the ARIS Module, which incorporates Islamic spiritual practices such as guided prayer, reflection, and recitation. Results: The intervention group showed a statistically significant reduction in anxiety and depression levels. Patients also reported increased peace, spiritual connection, and emotional stability, enhancing their coping abilities and overall quality of life. Conclusions: The ARIS Module effectively reduced anxiety and depression in CHD patients, highlighting the value of integrating spiritual care into CHD treatment for mental and emotional health. Suggestions: It is recommended to incorporate programs like ARIS module into healthcare settings, with further research to explore its impact across diverse patient populations.

Assessing the Effectiveness of a Structured Teaching-Learning Module in Improving Knowledge and Skills in Writing Injury Reports Among the Undergraduate Medical Students of a Tertiary Care Center in India.

Background The ability to write accurate and comprehensive injury reports is a crucial skill for medical professionals, particularly those working in emergency medicine and trauma care. A structured teaching-learning (TL) module can enhance the knowledge and skills of undergraduate medical students in this area. Aim The study aims to assess students' performance in cognitive, psychomotor, and communication domains of injury report writing before and after implementing the proposed structured TL module and compare the findings to evaluate the efficacy between the existing TL module and the proposed module. Methods A total of 50 undergraduateBachelor of Medicine, Bachelor of Surgery (MBBS) students of the tertiary care center consented to participate in the study. A total of 41 students participated throughout the entire study duration and were considered for data analysis. The performance of the students was evaluated through assessment and a structured grading description was utilized for distribution of marks. Data were collected, compiled, and analyzed with the help ofIBM SPSS Statistics for Windows, Version 16 (Released 2007; IBM Corp., Armonk, New York, United States). Results The percentage of students scoring higher than 60% marks in all three domains after the implementation of the proposed module in both immediate post-implementation assessment and two months after post-implementation assessment in comparison to pre-implementation assessment has been found to be statistically significant (p-value < 0.01). Conclusion The study observed improvement of performance in all three domains of the competency after implementation of the structured TL module which can be attributed to the introduction of more interactive sessions both in the form of small-group learning and bedside learning in the proposed module.

Effect of an online teaching module on midwives' knowledge, attitude and practice regarding intrapartum ultrasound: A quasi-experimental approach.

Intrapartum ultrasound (IPUS) allows for a more reliable and reproducible assessment of fetal head station and position during labor. This study aimed to investigate how an online education module on IPUS impacts midwives' knowledge, attitudes, and practices (KAP) regarding this topic. Midwives working in the labor ward of the University Hospital of Leuven (Belgium) were invited to participate in an educational program on IPUS in April 2023. A baseline KAP survey was completed upon enrolment, followed by an online education module on the intrapartum sonographic assessment of head station and position. Afterwards, a second KAP survey was completed. Score were compared using a two-sided Wilcoxon signed-rank test. A p<0.05 was considered significant. Statistical analyses were conducted using SPSS (version 29.0.2.0). A total of 45 midwives were eligible for inclusion and invited to participate. From these, 46.7% (21/45) agreed to take part. Attitudes towards IPUS were positive, as most perceived it as safe, time-efficient, and beneficial for medical-decision making. However, only two midwives (9.1%) sometimes used IPUS themselves during the second stage, whereas none used it during the first stage. A significant improvement in knowledge scores was recorded after the online education module for both the sections on fetal head station (median score 0/5 to 2.75/5, p=0.01) and position (median score 2.5/5 to 3.5/5, p=0.04). No significant differences were observed in the overall attitude scores, as they remained overall positive (4.5/5 to 5/5, p=0.18). Although having little experience with IPUS themselves, most participating midwives perceive it as an acceptable, time-efficient, and safe imaging modality. A short online education module resulted in a significant improvement in their knowledge of these topics. Further implementation research is needed to investigate how the uptake of IPUS amongst midwives can be improved, and how this can improve overall labor care.

“A Study To Assess The Effectiveness Of Self-Instructional Module (Sim) On Knowledge Regarding Quality Of Life Among Renal Failure Patients Admitted In Ah&amp;Rc Bg Nagara.”

“A Study To Assess The Effectiveness Of Self-Instructional Module (Sim) On Knowledge Regarding Quality Of Life Among Renal Failure Patients Admitted In Ah&amp;Rc Bg Nagara.”

Considering the Impact of a Tactical and Technical Training Module on the Performance of Field Hockey Players (U-16) in Malaysia: A Quasi-Experimental Design

Background. The development of tactical and technical skills is critical for young athletes engaged in team sports, particularly in field hockey, where quick decision-making and strategic play are essential for success. Objectives. This study aimed to evaluate the effectiveness of a structured tactical and technical training module on the performance of field hockey players (U-16) in Malaysia. Materials and methods. A quasi-experimental design was used, involving 40 participants who were randomly assigned to either an intervention group (n = 20) or a control group (n = 20). The intervention group underwent an eight-week tactical and technical training module, while the control group followed their regular training regimen. Pre-test and post-test assessments were conducted to measure knowledge and performance in four key areas: ball action/direction, opponents’ action, positioning, and action during changing situations. The data were analyzed using Multivariate Analysis of Variance (MANOVA) to assess the impact of the intervention. Results. The MANOVA results revealed significant improvements in the intervention group across all four variables compared to the control group (Wilks’ Lambda = 0.327, F = 9.867, p = 0.001). Specifically, the intervention group showed substantial gains in knowledge about ball action/direction, opponents’ action, positioning, and actions during changing situations. These findings underscore the effectiveness of the training module in enhancing tactical and technical competencies in young field hockey players. The study demonstrates that a structured tactical and technical training module can significantly improve the performance of under-16 field hockey players. Conclusions. The results indicate the potential for broader application of such modules in youth sports development programs. It is recommended that coaches integrate similar training structures to optimize player development and competitive performance.

EFFECTIVENESS OF A PROBLEM-BASED LEARNING MODULE ON SALT HYDROLYSIS IN IMPROVING STUDENT LEARNING OUTCOMES

This study aimed to analyze the effectiveness of the problem-based learning module on students' learning outcomes at SMAN 1 Bukittinggi. This study employed Plomp's development model. The research applies a quasi-experimental type with a non-equivalent control group design. The population includes phase F students of Class XI of SMAN 1 Bukittinggi who are studying chemistry in the 2023/2024 academic year as many as six classes. The sample was taken with a purposive sampling technique, and the selected classes XI F9 and XI F10. The test instrument was 20 multiple-choice questions. The data obtained were analyzed using the n-gain and hypothesis testing with the Mann-Whitney test. The results of data analysis obtained the n-gain value of the experimental class and control class, respectively, namely 0.74 with a high category and 0.58 with a medium category. As for hypothesis testing, the calculation of Asymps. Sig 0.05, then the learning outcomes of experimental class students are higher than those of the control class. The results showed that problem-based learning module learning on is effective in improving student learning outcomes at SMAN 1 Bukittingi.

Rapid Vehicle Trajectory Prediction Based on Multi-Attention Mechanism for Fusing Multimodal Information

Trajectory prediction plays a crucial role in autonomous driving tasks, as accurately and rapidly predicting the future trajectories of traffic participants can significantly enhance the safety and robustness of autonomous driving systems. This paper presents a novel trajectory prediction model that follows the encoder–decoder paradigm, achieving precise and rapid predictions of future vehicle trajectories by efficiently aggregating the spatiotemporal and interaction information of agents in traffic scenarios. We propose an agent–agent interaction information extraction module based on a sparse graph attention mechanism, which enables the efficient aggregation of interaction information between agents. Additionally, we introduce a non-autoregressive query generation method that accelerates the model inference speed by generating the decoding queries in parallel. Comparative experiments with the existing advanced algorithms show that our method improves the multimodal trajectory prediction metrics for the Minimum Average Displacement Error (minADE), the Minimum Final Displacement Error (minFDE), and the Miss Rate (MR) by an average of 9.1%, 11.8%, and 14.6%, respectively, while the inference time is only 33.7% of the average time taken by the other algorithms. Finally, we demonstrate the effectiveness of the various modules proposed in this paper through ablation studies.