Processing Model Research Articles

Generative AI model trained by molecular dynamics for rapid mechanical design of architected graphene

Generative artificial intelligence (AI) is shown to be a useful tool to automatically learn from existing information and generate new information based on their connections, but its usage for quantitative mechanical research is less understood. Here, we focus on the structure-mechanics relationship of architected graphene as graphene with void defects of specific patterns. We use Molecular Dynamics (MD) to simulate uniaxial tension on architected graphene, extract the von Mises stress field in mechanical loading, and use the results to train a fine-tuned generative AI model through a Low-Rank Adaptation method. This model enables the freely designed architected graphene structures and predicts its associated stress field in uniaxial tension loading through simple descriptive language. We demonstrate that the fine-tuned model can be established with a few training images and can quantitatively predict the stress field for graphene with various defect geometries and distributions not included in the training set. We validate the accuracy of the stress field with MD simulations. Moreover, we illustrate that our generative AI model can predict the stress field from a schematic drawing of the architected graphene through image-to-image generation. These features underscore the promising future for employing advanced generative AI models in end-to-end advanced nanomaterial design and characterization, enabling the creation of functional, structural materials without using complex numerical modeling and data processing.

Deciphering Digital Discourse: Detecting Cyberbullying Patterns in Filipino Tweets Using Machine Learning

The research addresses the escalating challenge of cyberbullying in the Philippines, a concern magnified by widespread social media use. A dataset of 146,661 tweets is analyzed using a pre-trained natural language processing model tailored to detect derogatory Filipino terms. The methodology is designed to preprocess data for clarity and analyze derogatory phrases, using the 23 key terms to indicate cyberbullying. Through quantitative analysis, specific patterns of derogatory term co-occurrence are uncovered. The research specifically focuses on Filipino digital discourse, uncovering patterns of derogatory language usage, which is unique to this context. Combining data mining and machine learning techniques, including Frequent Pattern (FP)-growth for pattern identification, cosine similarity for phrase correlation, and classification technique, the research achieves an accuracy rate of 97.91%. To assess the model’s reliability and precision, a 10-fold cross-validation is utilized. Moreover, by examining specific tweets, the analysis highlights the alignment between automated classifications and human judgment. The co-occurrence of derogatory terms, identified through methods like FP-growth and cosine similarity, reveals underlying cyberbullying narratives that are not immediately obvious. This approach validates the high accuracy of the models and emphasizes the importance of a comprehensive framework for detecting cyberbullying in a linguistically and culturally specific context. The findings substantiate the effectiveness of the targeted approach, providing essential insights for developing cyberbullying prevention strategies. Furthermore, the research enriches the literature on digital discourse analysis and online harassment prevention by addressing cyberbullying patterns and behaviors. Importantly, the research offers valuable guidance for policymakers in crafting more effective online safety measures in the Philippines.

Free Text Analysis: Identification of adverse drug events in clinical notes

ObjectiveTo explore the prevalence of adverse drug events (ADE) leading to drug changes using Artificial Intelligence (AI) based free text analysis of primary care encounter notes . ApproachWe used electronic medical record l encounter notes linked to population-based drug dispensation records. Dispensation records were used to identify drug switching to another agent in the same therapeutic class, which could suggest an undesirable side-effect requiring discontinuation of the drug. We annotated clinical notes by identifying ADEs after retrieving the last encounter note written for that patient on or before the identified date. We ran different AI models, including BERT and Large Language Models (LLMs), to identify ADEs. ResultsWe annotated 1085 notes with 362 ADEs. These were used to train BERT models (precision or PPV: 0.584 ) and prompt-based LLM (0.563) but with improved sensitivity (0.725) and specificity (0.739) . ConclusionsIdentifying ADEs requires deep medical knowledge and simple Natural Language Processing models trying to identify ADEs only on the basis of language of encounter notes was not successful based on the BERT model performance. Prompt based models have deep knowledge but were trained with the objective of producing grammatically correct, coherent sentences, not with the medical knowledge required. LLMs also require significant computational power. ImplicationsWhile AI does have significant potential to advance our capacity for complex analytic tasks the current LLMs are not yet able to adequately identify ADEs in a corpus of electronic clinical notes.

Generative AI: hopes, controversies and the future of faculty roles in education

Purpose Generative artificial intelligence (GAI) has seen exponential growth in recent years due to its capability to generate original content through natural language processing and comprehensive language models. This paper aims to investigate the transformative impact of GAI on higher education, focusing on the evolving roles of faculty in the classroom. Design/methodology/approach Using a phenomenological perspective and a process approach, the study involved 25 semi-structured interviews with academicians in higher education. Findings The findings reveal that GAI currently creates biased and commercially driven learning environments, challenging traditional pedagogical models. Despite its potential for enhancing education, the autonomous nature of GAI often prioritizes commercial interests over pedagogical goals. Research limitations/implications The study is limited to faculty perspectives, suggesting future research should include student viewpoints and diverse educational contexts. Practical implications The study highlights the need for higher education institutions to develop comprehensive policies, provide training for faculty and students and design new courses that leverage GAI for personalized learning experiences and enhanced faculty research. Originality/value This paper contributes to the emerging literature on GAI’s impact on education, highlighting its dual nature as both a transformative tool and a potential threat to traditional educational roles and outcomes.

Large language models outperform traditional natural language processing methods in extracting patient-reported outcomes in IBD.

Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement. This study aimed to compare traditional natural language processing (tNLP) and large language models (LLMs) in extracting three IBD PROs (abdominal pain, diarrhea, fecal blood) from clinical notes across two institutions. Clinic notes were annotated for each PRO using preset protocols. Models were developed and internally tested at the University of California San Francisco (UCSF), and then externally validated at Stanford University. We compared tNLP and LLM-based models on accuracy, sensitivity, specificity, positive and negative predictive value. Additionally, we conducted fairness and error assessments. Inter-rater reliability between annotators was >90%. On the UCSF test set (n=50), the top-performing tNLP models showcased accuracies of 92% (abdominal pain), 82% (diarrhea) and 80% (fecal blood), comparable to GPT-4, which was 96%, 88%, and 90% accurate, respectively. On external validation at Stanford (n=250), tNLP models failed to generalize (61-62% accuracy) while GPT-4 maintained accuracies >90%. PaLM-2 and GPT-4 showed similar performance. No biases were detected based on demographics or diagnosis. LLMs are accurate and generalizable methods for extracting PROs. They maintain excellent accuracy across institutions, despite heterogeneity in note templates and authors. Widespread adoption of such tools has the potential to enhance IBD research and patient care.

Integrative Approaches to Abiotic Stress Management in Crops: Combining Bioinformatics Educational Tools and Artificial Intelligence Applications

Abiotic stresses, including drought, salinity, extreme temperatures and nutrient deficiencies, pose significant challenges to crop production and global food security. To combat these challenges, the integration of bioinformatics educational tools and AI applications provide a synergistic approach to identify and analyze stress-responsive genes, regulatory networks and molecular markers associated with stress tolerance. Bioinformatics educational tools offer a robust framework for data collection, storage and initial analysis, while AI applications enhance pattern recognition, predictive modeling and real-time data processing capabilities. This review uniquely integrates bioinformatics educational tools and AI applications, highlighting their combined role in managing abiotic stress in plants and crops. The novelty is demonstrated by the integration of multiomics data with AI algorithms, providing deeper insights into stress response pathways, biomarker discovery and pattern recognition. Key AI applications include predictive modeling of stress resistance genes, gene regulatory network inference, omics data integration and real-time plant monitoring through the fusion of remote sensing and AI-assisted phenomics. Challenges such as handling big omics data, model interpretability, overfitting and experimental validation remain there, but future prospects involve developing user-friendly bioinformatics educational platforms, establishing common data standards, interdisciplinary collaboration and harnessing AI for real-time stress mitigation strategies in plants and crops. Educational initiatives, interdisciplinary collaborations and trainings are essential to equip the next generation of researchers with the required skills to utilize these advanced tools effectively. The convergence of bioinformatics and AI holds vast prospects for accelerating the development of stress-resilient plants and crops, optimizing agricultural practices and ensuring global food security under increasing environmental pressures. Moreover, this integrated approach is crucial for advancing sustainable agriculture and ensuring global food security amidst growing environmental challenges.

Design of real-time & historical database for photovoltaic power plant

Abstract The real-time & historical database of photovoltaic power plant monitoring system is used to store and manage the real-time and historical data, including power plant parameters, environmental data, and photovoltaic unit data and so on. Analyzing of this data is helpful for monitoring the status of the plant equipment, performing maintenance, and enhancing the operational efficiency of the power plant. The data models and database function of the database is designed. The data models include tags, data processing, and data storage models. Database function include the modules of the collection, storage, compression, and index query. The data collection module utilizes dynamic link libraries to access database data and configures parameters through a comprehensive data collection framework. The storage strategies involve in a combination of in-memory databases and disk-based historical databases, incorporating secondary caching technology and cloud server storage techniques. The data compression module is achieved through secondary compression methods. The index query module encompass time index files and the secondary index files to improve query speed and real-time system performance. The designed elements collectively establish an efficient monitoring system database.

Bitcoin Sentiment Analysis using Machine Learning Algorithms

The rapid rise of cryptocurrencies, particularly Bitcoin, has intensified research interest in understanding the factors influencing their price movements. Among these factors, social media sentiment has emerged as a crucial predictor, reflecting collective investor mood and market expectations. This paper provides a comprehensive survey of various sentiment analysis models applied in cryptocurrency markets, with a specific focus on the relationship between social media sentiment and Bitcoin price fluctuations. The study identifies the Aigents model as the most effective, showing significant improvements in predictive accuracy following fine-tuning. Findings reveal a predictive association between sentiment measures and price changes, typically with a latency of one to two days. The paper offers insights into the capabilities and limitations of existing Natural Language Processing (NLP) models in the context of cryptocurrency sentiment analysis, presenting practical implications for investors and analysts in navigating the volatile cryptocurrency markets. Key Words: cryptocurrency, Bitcoin, social media sentiment, natural language processing (NLP), sentiment analysis, price prediction, machine learning models, Aigents model, financial forecasting, Twitter, Reddit, artificial neural networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Random Forest, Naive Bayes, Extreme Gradient Boosting (XGBoost), predictive analytics, market behavior, interpretable AI.

Solving East Nile Delta's imaging challenges through new ocean-bottom-node acquisition and advanced imaging

The East Nile Delta, located offshore Egypt, is a challenging area for seismic imaging due to the presence of substantial velocity heterogeneity created by the complex Messinian unconformity and overlaying fault structures. Existing towed-streamer (TS) seismic data have been pushed to their technical limit but remain insufficient for adequate representation of the subsurface. Previous studies have shown that a step change in imaging is possible if advanced acquisition, processing, and velocity model building techniques are employed. Atoll Field is a candidate to benefit from such improvements in seismic data. Results show that ocean-bottom-node acquisition provides improved spatial sampling and higher resolution. Acquiring seismic data with offsets of more than 30 km facilitated the generation of a robust high-frequency velocity model that better captures subsurface complexity. This paper outlines the acquisition and processing, with a focus on the velocity model building process. The generated subsurface model is effectively used for imaging with conventional migration algorithms and full-waveform-inversion-derived reflectivity. Results show a significant step change from previous TS images at the Oligocene target, with channel features clearly imaged. This provides an improved understanding of the depositional systems and reduced depth uncertainty.

Validation of Non-Small Cell Lung Cancer Clinical Insights Using a Generalized Oncology Natural Language Processing Model.

Limited studies have used natural language processing (NLP) in the context of non-small cell lung cancer (NSCLC). This study aimed to validate the application of an NLP model to an NSCLC cohort by extracting NSCLC concepts from free-text medical notes and converting them to structured, interpretable data. Patients with a lung neoplasm, NSCLC histology, and treatment information in their notes were selected from a repository of over 27 million patients. From these, 200 were randomly selected for this study with the longest and the most recent note included for each patient. An NLP model developed and validated on a large solid and blood cancer oncology cohort was applied to this NSCLC cohort. Two certified tumor registrars and a curator abstracted concepts from the notes: neoplasm, histology, stage, TNM values, and metastasis sites. This manually abstracted gold standard was compared with the NLP model output. Precision and recall scores were calculated. The NLP model extracted the NSCLC concepts with excellent precision and recall with the following scores, respectively: Lung neoplasm 100% and 100%, NSCLC histology 99% and 88%, histology correctly linked to neoplasm 98% and 79%, stage value 98.8% and 92%, stage TNM value 93% and 98%, and metastasis site 97% and 89%. High precision is related to a low number of false positives, and therefore, extracted concepts are likely accurate. High recall indicates that the model captured most of the desired concepts. This study validates that Optum's oncology NLP model has high precision and recall with clinical real-world data and is a reliable model to support research studies and clinical trials. This validation study shows that our nonspecific solid tumor and blood cancer oncology model is generalizable to successfully extract clinical information from specific cancer cohorts.

ASSESSMENT OF THE FINANCIAL RESULT OF PUBLIC-PRIVATE PARTNERSHIP PROJECT IMPLEMENTATION USING THE EXAMPLE OF A CONCESSION AGREEMENT

The article is aimed at developing a methodological approach to improving the efficiency of assessing the financial results of implementing Ukrainian and European business projects. In this context, the object of study is a specific individual partnership and cooperation project. The research has established that one of the key informational aspects of such a partnership is the assessment and forecasting of the financial result. The goal of the research is to present the methodological approach to assessing financial performance for an individual project in the most detailed form possible. The article presents an approach based on modern simulation modelling methods, systems analysis methodology, and the method of composite seasonal indices. All modelling processes were conducted using MATLAB software. MATLAB is a high-level programming language and interactive environment for numerical computation, visualization, and programming. This program is widely used for modelling and data processing in fields such as engineering, scientific research, and mathematics. The research result is presented as an approach to the mathematical modelling of assessing the financial performance of partnership projects of Ukraine with representatives of EU business. It is necessary to emphasize that the presented research has limitations related to changes in Ukraine's hyperdynamic external environment provoked by war. Forecasts may vary depending on the development of military actions. Therefore, the prospects for further research should be directed towards improving the informational provision in the system of partnership and cooperation with EU countries to increase their own investment security.

A barking emotion recognition method based on Mamba and Synchrosqueezing Short-Time Fourier Transform

Dog barks are a crucial means for dogs to express their emotions, conveying needs and emotional states such as anger, sadness, or excitement. With the increasing number of pet dogs, understanding their emotional states is essential for better care and interaction. However, accurately interpreting the emotional content of dog barks is often challenging for humans. Therefore, developing a technology for recognizing the emotions in dog barks is of great importance. In this paper, we establish a dataset for dog bark emotion recognition and propose a method using the Synchrosqueezing Short-Time Fourier Transform (SST_STFT) to extract the time–frequency features of dog barks. Considering the characteristics of dog barks, we design an emotion recognition model based on Mamba. This model leverages the state-space model (Select SMM) for global modeling of long sequence features, enabling rapid and effective processing of time–frequency features and accurate perception of their variations. Experiments on the self-built DogEmotionSound dataset, as well as the public IEMOCAP and UrbanSound8K datasets, demonstrate that our model outperforms existing state-of-the-art sound recognition models across various evaluation metrics. The recognition accuracies on the three datasets are 91.97%, 95.36%, and 67.25%, respectively. The code is open-source at https://github.com/yangcjya/A-barking-emotion-recognition-method-based-on-Mamba.git.

Optimal quantum circuit generation for pixel segmentation in multiband images

A novel approach is proposed for multiband image processing via quantum models in real situations. Quantum circuits are automatically generated ad-hoc for each use case via multiobjective genetic algorithms. Using this universal method, image processing tasks such as segmentation can be carried out by considering the properties that constitute each pixel. The generated circuits present a low level of correlation between qubits, and thus can be considered quantum-inspired machine learning models. The effectiveness of this methodology has been validated by applying it to different segmentation use cases. Comparisons are made between optimized classical kernel methods and the generated quantum-inspired models to understand their behaviors. The results show that quantum models for multiband image processing achieve accuracies similar to those of classical methods.

In-line semantic segmentation of kimchi cabbage deterioration using YOLOv8n and DeepLabv3+

To ensure the safety of kimchi cabbage (KC), the effective identification of deterioration is required. In this study, we aimed to develop and validate a semantic segmentation pipeline to detect and segment the mild and severe deterioration of KC on moving industrial conveyor belts. The pipeline integrates the YOLOv8n and DeepLabv3+ models, image processing and semantic labelling models, respectively. The YOLOv8n model was employed for initial detection, achieving an F1 score (a measure of precision and recall) of 0.988 and an AP50 (average precision at 50 % intersection over union (IoU) with the ground truth) of 0.994. For detailed segmentation, the DeepLabv3+ model with Resnet101 and Mobilenetv3 backbones was used, and the performance of these models was evaluated based on F1 scores. The YOLOv8n model demonstrated high accuracy in detection. Further, the DeepLabv3+ model with the Resnet101 backbone achieved F1 scores of 0.772 and 0.851, and IoUs of 0.628 and 0.741 for mild and severe deterioration, respectively. Notably, the DeepLabv3+ model with Mobilenetv3 backbone excelled in identifying severe deterioration, indicating its potential for generating rapid alerts in edge devices. Crucially, the developed methodology enables the real-time, in-line monitoring of KC deterioration and is adaptable for future edge device applications. The results of this study highlight the potential of accessible technologies in advancing agricultural quality control, setting a benchmark for future research to expand data sources and enhance detection capabilities.

The Simulative Role of Neural Language Models in Brain Language Processing

The Simulative Role of Neural Language Models in Brain Language Processing

The effect of aging on the dual-route model of emotion processing applied to memory recognition.

Emotional faces are automatically processed in the human brain through a cortical route (conscious processing based on high spatial frequencies, HSF) and a subcortical route (subliminal processing based on low spatial frequencies, LSF). How each route contributes to emotional face recognition is still debated, and little is known about this process in aging. Here, 147 younger adults (YA) and 137 older adults (OA) were passively presented with neutral, happy, and angry faces, shown as (a) unfiltered, (b) filtered at LSF, and (c) hybrid (emotional LSF superimposed to the neutral HSF of the same face). In a succeeding recognition phase, the same faces and new faces were shown as unfiltered, and participants were asked whether each face had been already presented in the encoding phase. Despite the better performance by YA compared with OA for neutral faces presented as unfiltered (cortical route), the performance of OA was better than that of YA for angry faces presented as hybrid and for happy faces presented at LSF and as hybrid. We conclude that the activity of the subcortical route during the encoding phase facilitates emotional recognition in aging. Results are discussed in accordance with the dual-route model. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Automatic recognition system for concrete cracks with support vector machine based on crack features

Cracks are a common problem in concrete surfaces. With the continuous optimization of machine vision-based inspection systems, effective crack detection and recognition is the core of the entire system. In this study, support vector machine (SVM) was used to distinguish cracks from other regions. To complete the recognition system of the SVM, a framework consisting of an image processing and recognition model was proposed. An algorithm combining the Prewitt operator with the Otsu threshold was proposed for image segmentation. The binary image processed by the new algorithm combined with mathematical morphology can result in a more complete crack zone and fewer interference regions. After the initial parameter extraction, most of the impurity areas were screened by preliminary discrimination, removing 99% of the impurities. This processing step ensured the balance and effectiveness of the samples. To establish an automatic identification model based on SVM with a radial basis function, compactness, occupancy rate, and length–width ratio were selected as input parameters after comparing these three features with all six features of the crack. The recognition accuracy of this system reaches 97.14%, demonstrating that the proposed method is effective and satisfies practical requirements.

Evaluation technologies for assessing drought tolerance of Kimchi cabbage seedlings using hyperspectral imaging and principal component analysis

Conventionally, methods for evaluating the tolerance of kimchi cabbage have primarily relied on visual inspection. The development of evaluation criteria based on hyperspectral imaging may help address the problem of decision-making errors arising from visual inspection. However, to evaluate the tolerance of kimchi cabbage using hyperspectral imaging, not only are hyperspectral big data processing and sophisticated exponential model development required, but also real-time evaluation technology with high field usability. This study presents an evaluation method for extracting vegetation information from hyperspectral data, logically analyzing the extracted data, validating the developed model, and ensuring field utility. The red-edge band between 680 and 700 nm is for evaluating the drought tolerance of Kimchi cabbage. The proposed method allows for assessing the drought tolerance of Kimchi cabbage seedlings within a range of 1 to 30, using a robust index with a variance of approximately 2.25 and a sensitivity index with a variance of around 8.4. Since the proposed nonlinear drought tolerance evaluation method must strongly correlate with the chlorophyll index, its performance was verified using the newly proposed cross-point analyzer. While nonlinear drought evaluation technology can serve as a robust analysis method based on extensive data, it has the disadvantage of requiring a long evaluation time. However, the additionally developed cross-point analyzer can improve the real-time capability of testing. Proper utilization of these two analytical technologies enhances the performance of robust and rapid drought tolerance evaluation for Kimchi cabbage seedlings. Therefore, this study introduces two techniques for non-destructively and quantitatively explaining plant water stress.

Taking It Easy: Off-the-Shelf Versus Fine-Tuned Supervised Modeling of Performance Appraisal Text

When assessing text, supervised natural language processing (NLP) models have traditionally been used to measure targeted constructs in the organizational sciences. However, these models require significant resources to develop. Emerging “off-the-shelf” large language models (LLM) offer a way to evaluate organizational constructs without building customized models. However, it is unclear whether off-the-shelf LLMs accurately score organizational constructs and what evidence is necessary to infer validity. In this study, we compared the validity of supervised NLP models to off-the-shelf LLM models (ChatGPT-3.5 and ChatGPT-4). Across six organizational datasets and thousands of comments, we found that supervised NLP produced scores were more reliable than human coders. However, and even though not specifically developed for this purpose, we found that off-the-shelf LLMs produce similar psychometric properties as supervised models, though with slightly less favorable psychometric properties. We connect these findings to broader validation considerations and present a decision chart to guide researchers and practitioners on how they can use off-the-shelf LLM models to score targeted constructs, including guidance on how psychometric evidence can be “transported” to new contexts.

Exploring the application of ChatGPT in learning English of students – A study on perception of sophomores at Nguyen Tat Thanh University

This research focuses on analysing the application of ChatGPT, which is a natural language processing model, in learning English at the university level. A survey was conducted in 244 sophomore at Nguyen Tat Thanh University, to investigate their opinions on the use of ChatGPT in education. Results indicate that the majority of students hold a positive view towards using ChatGPT in learning English and believe that it can help improve their English skills. The research also proposes several ways to enhance the effectiveness of using ChatGPT in learning. In summary, this study highlights the potential of ChatGPT in supporting English education at universities. ® 2024 Journal of Science and Technology - NTTU