Development Of Model Research Articles

Development of Project-Based Learning Oriented Student Worksheets to Improve Students' Creative Thinking Skills on Class X High School Nanotechnology Material

The purpose of this study is to determine: the feasibility of project-based learning-oriented student worksheets in terms of content validity and construct validity, and the effectiveness of project-based learning-oriented student worksheets in terms of increasing students' creative thinking skills. Development of Project-Based Learning oriented student worksheets to improve students' creative thinking skills on class X high school nanotechnology material using the Research and Development (R&D) method with a 4D development model (Define, Design, Develop, and Disseminate). However, this research was carried out only until the third stage, namely Develop. The results of data analysis and discussion are as follows: the worksheets of project-based learning-oriented students developed are declared to have met the eligibility requirements in terms of content validity and construct validity with mode (Mo) values ≥ 4 and R ≥ 75%, and student worksheets project-oriented based learning developed was declared effective in terms of increasing students' creative thinking ability with a percentage of N-Gain of 79% moderate category (0.30-0.70) and 21% high category (0.7-1). Thus, Project Based Learning Oriented Student Worksheets can be used to improve students' creative thinking skills on Nanotechnology material.

Creep Phenomena, Mechanisms, and Modeling of Complex Engineering Alloys

Metal creep has been a subject of extensive study for more than 110 years because it affects the useful life of engineering components operating at high temperatures. This is even more true with ever-increasing operating temperatures of propulsion/power-generation systems and the environmental regulations to reduce greenhouse emissions. This review summarizes the recent development in creep modeling with regards to creep strain evolution, creep rate, creep ductility, creep life, and fracture mode, attempting to provide a comprehensive mechanism-based framework to address all the important creep phenomena and the long-standing issue of long-term creep life prediction with microstructural evolution and environmental effects.

Comprehensive Survey of Abstractive Text Summarization Techniques

Text summarization using pre-trained encoders has become a crucial technique for efficiently managing large volumes of text data. The rise of automatic summarization systems addresses the need to process ever-increasing data while meeting user-specific requirements. Recent scientific research highlights significant advancements in abstractive summarization, with a particular focus on neural network-based methods. A detailed review of various neural network models for abstractive summarization identifies five key components essential to their design: encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset selection, and evaluation metrics. Each of these elements is pivotal in enhancing the summarization process. This study aims to provide a thorough understanding of the latest developments in neural network-based abstractive summarization models, offering insights into the evolving field and underscoring the associated challenges. Qualitative analysis using a concept matrix reveals common design trends in contemporary neural abstractive summarization systems. Notably, BERT-based encoder-decoder models have emerged as leading innovations, representing the most recent progress in the field. Based on the insights from this review, the study recommends integrating pre-trained language models with neural network techniques to achieve optimal performance in abstractive summarization tasks. As the volume of online information continues to surge, the field of automatic text summarization has garnered significant attention within the Natural Language Processing (NLP) community. Spanning over five decades, researchers have approached this problem from diverse angles, exploring various domains and employing a multitude of paradigms. This survey aims to delve into some of the most pertinent methodologies, focusing on both single-document and multiple-document summarization techniques, with a particular emphasis on empirical methods and extractive approaches. Additionally, the survey explores promising strategies that target specific intricacies of the summarization task. Notably, considerable attention is dedicated to the automatic evaluation of summarization systems, recognizing its pivotal role in guiding future research endeavors.

Development of Application-based Interactive Science E-Modules PocketBook to Improve the Digital Literacy and Creative Thinking of Middle School Students

This research aims to develop an application-based interactive Science E-Module PocketBook to improve the digital literacy and creative thinking of junior high school students. This research method used research and development with a 4D model (Define, Design, Develop, and Disseminate). Validation of the E-Module was carried out at the Jember University Master of Science Education campus while the validity test, practicality test, and effectiveness test of the application-based interactive Science E-Module PocketBook was carried out at SMPN 10 Jember and SMPN 12 Jember. The subjects in this development were class VIII even semester students. Results This research showed that the average validity score obtained is 86% by expert validators. The percentage of response results from the limited scale and class scale tests at SMPN 10 Jember were respectively 85.1% and 86.6%, each score can be categorized as very valid. Application-based interactive science e-module PocketBook It not only contains material but there are also assignments to improve students' digital literacy and creative thinking. Application-based interactive science e-module PocketBook the theme of vibrations and waves has also been distributed to two classes at SMPN 12 Jember with student response scores of 81.9% and 85.3% respectively.

Transforming Digital Marketing with Generative AI

The current marketing landscape faces challenges in content creation and innovation, relying heavily on manually created content and traditional channels like social media and search engines. While effective, these methods often lack the creativity and uniqueness needed to stand out in a competitive market. To address this, we introduce MARK-GEN, a conceptual framework that utilises generative artificial intelligence (AI) models to transform marketing content creation. MARK-GEN provides a comprehensive, structured approach for businesses to employ generative AI in producing marketing materials, representing a new method in digital marketing strategies. We present two case studies within the fashion industry, demonstrating how MARK-GEN can generate compelling marketing content using generative AI technologies. This proposition paper builds on our previous technical developments in virtual try-on models, including image-based, multi-pose, and image-to-video techniques, and is intended for a broad audience, particularly those in business management.

Transforming Digital Marketing with Generative AI

The current marketing landscape faces challenges in content creation and innovation, relying heavily on manually created content and traditional channels like social media and search engines. While effective, these methods often lack the creativity and uniqueness needed to stand out in a competitive market. To address this, we introduce MARK-GEN, a conceptual framework that utilises generative artificial intelligence (AI) models to transform marketing content creation. MARK-GEN provides a comprehensive, structured approach for businesses to employ generative AI in producing marketing materials, representing a new method in digital marketing strategies. We present two case studies within the fashion industry, demonstrating how MARK-GEN can generate compelling marketing content using generative AI technologies. This proposition paper builds on our previous technical developments in virtual try-on models, including image-based, multi-pose, and image-to-video techniques, and is intended for a broad audience, particularly those in business management.

Artificial Neural Network Learning, Attention, and Memory

The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a dual network is outlined acting between synapses of a basic ANN, which controls the learning process and coordinates the subnetworks selected by attention mechanisms toward purposeful behaviors. Mechanisms of memory and their affinity with comprehension are considered, by emphasizing the common role of abstraction and the interplay between assimilation and accommodation, in the spirit of Piaget’s analysis of psychological acquisition and genetic epistemology. Learning, comprehension, and knowledge are expressed as different levels of organization of informational processes inside cognitive systems. It is argued that formal analyses of cognitive artificial systems could shed new light on typical mechanisms of “natural intelligence” and, in a specular way, that models of natural cognition processes could promote further developments of ANN models. Finally, new possibilities of chatbot interaction are briefly discussed.

The changing landscape of text mining: a review of approaches for ecology and evolution.

In ecology and evolutionary biology, the synthesis and modelling of data from published literature are commonly used to generate insights and test theories across systems. However, the tasks of searching, screening, and extracting data from literature are often arduous. Researchers may manually process hundreds to thousands of articles for systematic reviews, meta-analyses, and compiling synthetic datasets. As relevant articles expand to tens or hundreds of thousands, computer-based approaches can increase the efficiency, transparency and reproducibility of literature-based research. Methods available for text mining are rapidly changing owing to developments in machine learning-based language models. We review the growing landscape of approaches, mapping them onto three broad paradigms (frequency-based approaches, traditional Natural Language Processing and deep learning-based language models). This serves as an entry point to learn foundational and cutting-edge concepts, vocabularies, and methods to foster integration of these tools into ecological and evolutionary research. We cover approaches for modelling ecological texts, generating training data, developing custom models and interacting with large language models and discuss challenges and possible solutions to implementing these methods in ecology and evolution.

To establish a prognostic model of epidermal growth factor receptor mutated non-small cell lung cancer patients based on Least Absolute Shrinkage and Selection Operator regression.

There is currently a shortage of effective diagnostic tools that are used for identifying long-term survival among non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations. This research utilized the development of a prognostic model to assist clinicians in forecasting the survival over 24 months. In Phase III and IV those patients who were diagnosed with EGFR mutation from January 2018 to June 2022 were enrolled into the lung cancer group of Thoracic Surgery Department of Hebei Provincial People's Hospital. Long-run survival was stated as survival for 24 months after being diagnosed. A multivariate prognostic pattern was constructed by means of internal validation and binary logistic regression by bootstrapping. One nomogram was created with a view to boosting the explanation and applicability of the pattern. A total of 603 patients with EGFR mutation were registered. Elements linked to the whole survival beyond 24 months were age (OR 6.15); female (OR 1.79); functional status (ECOG 0-1) (OR 5.26); Exon 20 insertion mutation deletion (OR 2.08); No central nervous system metastasis (OR 2.66), targeted therapy (OR 0.43); Immunotherapy (OR 0.24). The model has good internal validation. Seven pretreatment clinicopathological variables predicted survival over 24 months. That pattern owns a great discriminative capability. It is hypothesized that this pattern is capable of assisting in selecting the optimal treatment sequence for NSCLC patients with EGFR mutations.

A comprehensive survey for generative data augmentation

Generative data augmentation (GDA) has emerged as a promising technique to alleviate data scarcity in machine learning applications. This thesis presents a comprehensive survey and unified framework of the GDA landscape. We first provide an overview of GDA, discussing its motivation, taxonomy, and key distinctions from synthetic data generation. We then systematically analyze the critical aspects of GDA—selection of generative models, techniques to utilize them, data selection methodologies, validation approaches, and diverse applications. Our proposed unified framework categorizes the extensive GDA literature, revealing gaps such as the lack of universal benchmarks. The thesis summarizes promising research directions, including , effective data selection, theoretical development for large-scale models’ application in GDA and establishing a benchmark for GDA. By laying a structured foundation, this thesis aims to nurture more cohesive development and accelerate progress in the vital arena of generative data augmentation.

Chinese Title Generation for Short Videos: Dataset, Metric and Algorithm.

Previous work for video captioning aims to objectively describe the video content but the captions lack human interest and attractiveness, limiting its practical application scenarios. The intention of video title generation (video titling) is to produce attractive titles, but there is a lack of benchmarks. This work offers CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title gEneration dataset, to assist research and applications in video titling, video captioning, and video retrieval in Chinese. CREATE comprises a high-quality labeled 210 K dataset and two web-scale 3 M and 10 M pre-training datasets, covering 51 categories, 50K+ tags, 537K+ manually annotated titles and captions, and 10M+ short videos with original video information. This work presents ACTEr, a unique Attractiveness-Consensus-based Title Evaluation, to objectively evaluate the quality of video title generation. This metric measures the semantic correlation between the candidate (model-generated title) and references (manual-labeled titles) and introduces attractive consensus weights to assess the attractiveness and relevance of the video title. Accordingly, this work proposes a novel multi-modal ALignment WIth Generation model, ALWIG, as one strong baseline to aid future model development. With the help of a tag-driven video-text alignment module and a GPT-based generation module, this model achieves video titling, captioning, and retrieval simultaneously. We believe that the release of the CREATE dataset, ACTEr metric, and ALWIG model will encourage in-depth research on the analysis and creation of Chinese short videos.

Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.

Recent Insights in Noninvasive Diagnostic for the Assessment of Kidney and Cardiovascular Outcome in Kidney Transplant Recipients.

Kidney transplantation improves quality of life and prolongs survival of patients with end-stage kidney disease. However, kidney transplant recipients present a higher risk for cardiovascular events compared to the general population. Risk assessment for graft failure as well as cardiovascular events is still based on invasive procedures. Biomarkers in blood and urine, but also new diagnostic approaches like genetic or molecular testing, can be useful tools to monitor graft function and to identify patients of high cardiovascular risk. Many biomarkers have been introduced, whereas most of these biomarkers have not been implemented in clinical routine. Here, we discuss recent developments in biomarkers and diagnostic models in kidney transplant recipients. Because many factors impact graft function and cardiovascular risk, it is most likely that no biomarker will meet the highest demands and standards. We advocate to shift focus to the identification of patients benefitting from molecular and genetic testing as well as from analysis of more specific biomarkers instead of finding one biomarker fitting to all patients.

PENTA as a model for advanced integrated cancer management in low-resource settings.

e13783 Background: Cancer models are reactive, fragmented, and volume-based care, in which the lack of coordination between different layers of care, along with clinical and quality variability, technological pressure, and dominant positions, result in high levels of inefficiency and waste, affecting economic sustainability and hence effective access. This is prohibitive for low-resource settings. Modern healthcare system models in this scenarios should focus on solve this problems. Comprehensive cancer management needs a reorientation towards value- and efficiency-generating architectures.Usual redesigns have focused on direct cost containment through intensified audits or new contracting and payment modalities between stakeholders. However, the real impact of these strategies is marginal, increasing sectoral mistrust and failing to meet the quintuple aim. PENTA is a comprehensive cancer management framework to optimize cancer patient care, with emphasis on clinical governance, data guidance, and clinical-administrative alignment. This is an operational model for stakeholders in low-resource settings. PENTA model has been successfully implemented in health insurance companies in Colombia and in private insurance branch in Peru. Methods: PENTA has been applied since 2020. We employed a work methodology based on the Agile-2 matrix, which allows for fluidity and lower resource consumption. This methodology is hybrid and its execution begins with an initial diagnosis of the process within the partner company. This diagnosis includes prioritizing interventions from each of the axes, but understanding that this is a systemic project that includes 5 fundamental axes. This axes must be worked on simultaneously and interdependently to achieve the greatest value. Axes are: 1. Smart Risk Management, 2. Advanced Clinical Gobernance, 3. Value Audit, 4. Technology Management and 5. Value-based Contracting Focus on managing inefficiencies and waste (optimization model), PENTA can generate early results across various dimensions such as clinical variability and technology costs. Structuring and using a cross-cutting data architecture is fundamental to the model's development into a data control tower. Results: Implementation of a data control tower for cancer cohorts , development of joint clinical pathways (insurer-payer) for prioritized diseases, clinical practice agreements with hierarchical positions on high-cost medications, implementation of mini health technology assessments (HTAs), and generation of new prospective payment modalities based on performance adjustment. The estimated savings generated are $1.5 million for the phase 2 in Colombia and $800,000 for the phase 1 in Peru. Conclusions: PENTA is an agile framework for cancer management that can be sucessfully adapted to low-resource settings.

Environmental public opinion: methodological principles of research

Based on the structural analysis of the toolkit of the foreign monitoring studies, the methodological principles of measuring environmental public opinion are analyzed. Modern developments of socio-psychological models for predicting environmental concern and patterns of environmental behavior, the influence of the social context on environmental activism, the influence of methods of measuring environmental concern on conclusions regarding the relationship between material and post-material values are considered in detail. It is noted that traditional indicators of environmental concern are replaced by tools of sociological measurement of environmental awareness, primarily of anthropocentrism and its relationship with ecocentrism. The need to transition from the (post-Soviet) ecological paradigm to the modern European one, the synchronization of advancement to full membership in the EU with the transit of Ukrainian society from the patronage to the liberal model of democracy determine the features of the social context of measuring environmental public opinion in Ukraine. The specified features of the social context require a radical revision of the methodological principles of measuring environmental public opinion with relevant sociological indicators. First, we are talking about the development of indicators of environmental democracy, such as the level of access to environmental information, participation in the adoption of environmentally important decisions, and access to justice on environmental issues. It is argued that the main task of monitoring environmental public opinion is to identify the dynamics of social changes as a derivative of anthropogenic impact on the environment, to adjust environmental policy considering the existing ecological orientations of the population.

Development of a Novel Nonlinear Dynamic Cavitation Model and Its Numerical Validations

Development of a Novel Nonlinear Dynamic Cavitation Model and Its Numerical Validations

The role of early cerebral edema and hematoma assessment in aneurysmal subarachnoid hemorrhage (a-SAH) in predicting structural brain abnormalities in cognitive impairments-- case controlled study.

Early assessment and management of cerebral edema and hematoma following aneurysmal subarachnoid hemorrhage (a-SAH) can significantly impact clinical cognitive outcomes. However, current clinical practices lack predictive models to identify early structural brain abnormalities affecting cognition. To address this gap, we propose the development of a predictive model termed the a-SAH Early Brain Edema/Hematoma Compression Neural (Structural Brain) Networks Score System (SEBE-HCNNSS). In this study, 202 consecutive patients with spontaneous a-SAH underwent initial computed tomography (CT) or magnetic resonance imaging (MRI) scans within 24 hours of ictus with follow-up 2 months after discharge. Using logistic regression analysis (univariate and multivariate), we evaluated the association of clinically relevant factors and various traditional scale ratings with cognitive impairment (CI). Risk factors with the highest area under the curve (AUC) values were included in the multivariate analysis and least absolute shrinkage and selection operator (LASSO) analysis or Cox regression analysis. A total of 177 patients were enrolled in the study, and 43 patients were classified with a high SEBE-HCNNSS grade (3 to 5). After a mean follow-up of 2 months, 121 individuals (68.36%) with a-SAH and 3 control subjects developed incident CI. The CT inter-observer reliability of the SEBE-HCNNSS scale was high, with a Kappa value of 1. Furthermore, ROC analysis identified the SEBE-HCNNSS scale (OR 3.322, 95% CI 2.312-7.237, P=0.00025) as an independent predictor of edema, CI, and unfavorable prognosis. These results were also replicated in a validation cohort. Overall, the SEBE-HCNNSS scale represents a simple assessment tool with promising predictive value for CI and clinical outcomes post-a-SAH. Our findings indicate its practical utility as a prognostic instrument for risk evaluation after a-SAH, potentially facilitating early intervention and treatment.

Unsupervised Domain Adaptation of Object Detectors: A Survey.

Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection. However, learning highly accurate models relies on the availability of large-scale annotated datasets. Due to this, model performance drops drastically when evaluated on label-scarce datasets having visually distinct images, termed as domain adaptation problem. There are a plethora of works to adapt classification and segmentation models to label-scarce target dataset through unsupervised domain adaptation. Considering that detection is a fundamental task in computer vision, many recent works have focused on developing novel domain adaptive detection techniques. Here, we describe in detail the domain adaptation problem for detection and present an extensive survey of the various methods. Furthermore, we highlight strategies proposed and the associated shortcomings. Subsequently, we identify multiple aspects of the problem that are most promising for future research. We believe that this survey shall be valuable to the pattern recognition experts working in the fields of computer vision, biometrics, medical imaging, and autonomous navigation by introducing them to the problem, and familiarizing them with the current status of the progress while providing promising directions for future research.

Face mask classification using convolutional neural networks with facial image regions and super resolution

<p>Face mask classification is relevant to public health and safety, so an approach for face mask classification using Multi-Task Cascaded Convolutional Networks (MTCNN) for face detection on image data, ResNet152 architecture for feature extraction, and super-resolution method, BSRGAN, for enhanced image quality was proposed. The classification model was trained by a fully connected layer of neural networks. The goal is to classify each facial image into three classes: the image with a mask, without a mask, or with an incorrectly worn mask. The performance of each classification model on two real-world datasets was evaluated by Accuracy, Precision, Recall, and F1 score for different sets of input patterns which were features extracted from the facial image regions including their combinations. Using multiple image regions, i.e. face, nose, and mouth, as resources for preparing input features showed the improved classification performance compared to using single image regions. In addition, the super-resolution technique applied to medium or large-sized images can improve the performance of the face mask classification model. Our findings may further guide the development for greater effective models and techniques on face mask classification contributing to practical scenarios.</p>

The machine learning-based model for lateral lymph node metastasis of thyroid medullary carcinoma improved the prediction ability of occult metastasis.

For medullary thyroid carcinoma (MTC) with no positive findings in the lateral neck before surgery, whether prophylactic lateral neck dissection (LND) is needed remains controversial. A better way to predict occult metastasis in the lateral neck is needed. From January 2010 to January 2022, patients who were diagnosed with MTC and underwent primary surgery at our hospital were retrospectively reviewed. We collected the patients' baseline characteristics, surgical procedure, and rescored the ultrasound images of the primary lesions using American College of Radiology (ACR) Thyroid Imaging, Reporting and Data System (TI-RADS). Regularized logistic regression, 5-fold cross-validation and decision curve analysis was applied for lateral lymph node metastasis (LLNM) model's development and validation. Then, we tested the predictive ability of the LLNM model for occult LLNM in cN0-1a patients. A total of 218 patients were enrolled. Five baseline characteristics and two TI-RADS features were identified as high-risk factors for LLNM: gender, baseline calcitonin (Ctn), tumor size, multifocality, and central lymph node (CLN) status, as well as TI-RADS margin and level. A LLNM model was developed and showed a good discrimination with 5-fold cross-validation mean area under curve (AUC) = 0.92 ± 0.03 in the test dataset. Among cN0-1a patients, our LLNM model achieved an AUC of 0.91 (95% CI, 0.88-0.94) for predicting occult LLNM, which was significantly higher than the AUCs of baseline Ctn (0.83) and CLN status (0.64). We developed a LLNM prediction model for MTC using machine learning based on clinical baseline characteristics and TI-RADS. Our model can predict occult LLNM for cN0-1a patients more accurately, then benefit the decision of prophylactic LND.