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Although interdisciplinary research has garnered extensive attention in academia, its core knowledge structure has yet to be systematically explored. To address this gap, this study aims to uncover the underlying core knowledge topics within interdisciplinary research, enabling researchers to gain a deeper understanding of the knowledge framework, improve research efficiency, and offer insights for future inquiries. Based on the Web of Science (WoS) database, this study collected 153 highly cited papers and employed the LDA topic model to identify latent topics and extract the knowledge structure within interdisciplinary research. The findings indicate that the core knowledge topics of interdisciplinary research can be categorized into four major areas: the knowledge framework and social impact of interdisciplinary research, multidisciplinary approaches in cancer treatment and patient care, Covid-19 multidisciplinary care and rehabilitation, and multidisciplinary AI and optimization in industrial applications. Moreover, the study reveals that AI-related interdisciplinary research topics are rapidly emerging. Through an in-depth analysis of these topics, the study discusses potential future directions for interdisciplinary research, including the cultivation and development of interdisciplinary talent, evaluation systems and policy support for interdisciplinary research, international cooperation and interdisciplinary globalization, and AI and interdisciplinary research optimization. This study not only uncovers the core knowledge structure of interdisciplinary research but also demonstrates the effectiveness of the LDA topic model as a data mining tool for revealing key topics and trends, providing practical tools for future research. However, this study has two main limitations: the time lag of highly cited papers and the dynamic evolution of interdisciplinary research. Future research should address these limitations to further enhance the understanding of interdisciplinary research. [ABSTRACT FROM AUTHOR]

Background/Objectives: Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. Methods: This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). Results: The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. Conclusions: This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease. [ABSTRACT FROM AUTHOR]

Accurately classifying road signs is crucial for autonomous driving due to the high stakes involved in ensuring safety and compliance. As Convolutional Neural Networks (CNNs) have largely replaced traditional Machine Learning models in this domain, the demand for substantial training data has increased. This study aims to compare the performance of classical Machine Learning (ML) models and Deep Learning (DL) models under varying amounts of training data, particularly focusing on altered signs to mimic real-world conditions. We evaluated three classical models: Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA), and one Deep Learning model: Convolutional Neural Network (CNN). Using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which includes approximately 40,000 German traffic signs, we introduced digital alterations to simulate conditions such as environmental wear or vandalism. Additionally, the Histogram of Oriented Gradients (HOG) descriptor was used to assist classical models. Bayesian optimization and k-fold cross-validation were employed for model fine-tuning and performance assessment. Our findings reveal a threshold in training data beyond which accuracy plateaus. Classical models showed a linear performance decrease under increasing alteration, while CNNs, despite being more robust to alterations, did not significantly outperform classical models in overall accuracy. Ultimately, classical Machine Learning models demonstrated performance comparable to CNNs under certain conditions, suggesting that effective road sign classification can be achieved with less computationally intensive approaches. [ABSTRACT FROM AUTHOR]

Simple Summary: Left displaced abomasum is a common disease among Holstein cattle that is causing significant economic losses. The exact causes of the disease are unknown, but it is assumed that LDA is multifactorial in nature. Predisposition to the displacement has been shown to be inherited. We conducted a genome-wide study to identify statistical associations between certain mutations and left displaced abomasum in Holstein cows from several farms in the Leningrad region. We found suggestive statistical associations with this disease and several mutations located in a set of genes involved in a variety of physiological processes. Some of these processes may be directly related to the development of the disease being studied. In addition, we conducted runs of homozygosity analysis and found that cows with displaced abomasum are more likely than healthy cows to carry a specific state of genomic regions containing genes associated with body shape, including width. This may indicate the role of this trait in the development of the disease. Left displaced abomasum (LDA) is a multifactorial disease of cattle that occurs mainly during the transition postpartum period and is characterized by a decrease in milk production and an increased risk of culling. Several studies have been conducted confirming the hereditary nature of predisposition to this disease. The aim of our study is to identify genetic associations characterizing the genomic variability of susceptibility to LDA in Holstein cattle of the Leningrad region of the Russian Federation. The objects of this study were 360 highly productive dairy cows divided into two groups: animals with LDA, and healthy ones (control). Runs of homozygosity analysis revealed one ROH on BTA13 that was found to be significantly more prevalent in the group of animals with LDA than in the healthy group. Fourteen candidate SNPs were found to be nominally associated with left displacement of the abomasum (p-value < 1 × 10−4). When performing functional annotation of genes containing associated polymorphisms or located close to them, candidate genes presumably associated with the development of LDA were identified: ABCB11, SRP72, RGS18, SOX4, GSG1L, FBXL19, and PNPLA4. [ABSTRACT FROM AUTHOR]

Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this research, Metacritic game reviews for Elden Ring were analyzed for topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid model combining both to identify effective methods for extracting underlying themes in player feedback. We analyzed and interpreted these models' outputs to learn the game reviews. We aimed to identify the differences, similarities, and variations between the three to determine which provided more valuable and instructive information. Our findings indicate that each method successfully identified keywords with some similarities in identified words. The LDA model had the highest silhouette score, indicating the most distinct clustering. The LDA-BERT model had a 1% higher coherence score than LDA, indicating more meaningful topics. [ABSTRACT FROM AUTHOR]

The ability to differentiate species is not adequate for modern forage breeding programs. The measurement of persistence is currently a bottleneck in the breeding system that limits the throughput of cultivars to the marketplace and prevents it from being selected as a trait. The use of hyperspectral data obtained through remote sensing offers the potential to reduce guesswork by identifying the distribution of pasture species, but only if such data alone can distinguish the subtle differences within species, i.e., cultivars. The implementation of this technology faces many challenges due to the spectral and temporal variability of species. To understand the spectral variability between and within species groups, differentiation using hyperspectral data from monoculture plots of turf species was utilized. Spectral data were collected over a year using an ASD FieldSpec® and canopy pasture probe (CAPP). The plots consisted of monocultures of various species, and cultivars (a total of 10 plots). Linear discriminant analysis (LDA) was conducted on the full spectrum and reduced band data. This technique successfully differentiated the species with high accuracy (>98%). We demonstrate the potential of hyperspectral data and analysis techniques to accurately separate differences down to cultivar level. We also show that diurnal variation is measurable in the spectra but does not preclude differentiation. [ABSTRACT FROM AUTHOR]

Background: Kratom is a substance that alters one's mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom's benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom's effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor embedding (t-SNE) technique to identify kratom's benefits and adverse effects. Results: The analysis showed that kratom aids in addiction recovery and managing opiate withdrawal, alleviates anxiety, depression, and chronic pain, enhances mood, energy, and overall mental well-being, and improves quality of life. Conversely, it may induce nausea, upset stomach, and constipation, elevate heart risks, affect respiratory function, and threaten liver health. Additional reported side effects include brain damage, weight loss, seizures, dry mouth, itchiness, and impacts on sexual function. Conclusion: This combined approach underscores its effectiveness in providing a comprehensive understanding of diverse reactions to kratom, complementing traditional research methodologies used to study kratom. [ABSTRACT FROM AUTHOR]

Scientific knowledge of image-based crack detection methods is limited in understanding their performance across diverse crack sizes, types, and environmental conditions. Builders and engineers often face difficulties with image resolution, detecting fine cracks, and differentiating between structural and non-structural issues. Enhanced algorithms and analysis techniques are needed for more accurate assessments. Hence, this research aims to generate an intelligent scheme that can recognize the presence of cracks and visualize the percentage of cracks from an image along with an explanation. The proposed method fuses features from concrete surface images through a ResNet-50 convolutional neural network (CNN) and curvelet transform handcrafted (HC) method, optimized by linear discriminant analysis (LDA), and the eXtreme gradient boosting (XGB) classifier then uses these features to recognize cracks. This study evaluates several CNN models, including VGG-16, VGG-19, Inception-V3, and ResNet-50, and various HC techniques, such as wavelet transform, counterlet transform, and curvelet transform for feature extraction. Principal component analysis (PCA) and LDA are assessed for feature optimization. For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. To isolate and quantify the crack region, this research combines image thresholding, morphological operations, and contour detection with the convex hulls method and forms a novel algorithm. Two explainable AI (XAI) tools, local interpretable model-agnostic explanations (LIMEs) and gradient-weighted class activation mapping++ (Grad-CAM++) are integrated with the proposed method to enhance result clarity. This research introduces a novel feature fusion approach that enhances crack detection accuracy and interpretability. The method demonstrates superior performance by achieving 99.93% and 99.69% accuracy on two existing datasets, outperforming state-of-the-art methods. Additionally, the development of an algorithm for isolating and quantifying crack regions represents a significant advancement in image processing for structural analysis. The proposed approach provides a robust and reliable tool for real-time crack detection and assessment in concrete structures, facilitating timely maintenance and improving structural safety. By offering detailed explanations of the model's decisions, the research addresses the critical need for transparency in AI applications, thus increasing trust and adoption in engineering practice. [ABSTRACT FROM AUTHOR]

The study empirically analyzed activism participants' roles drawn from the lens of social media affordance and identified the activism opinion leaders based on the framework of network connectivity, message diffusion, and semantic relevancy through the case of the #Marchforourlives Twitter network, which has been rebranded as X. The study defines the #Marchforourlives Twitter network as a co-created activism network in collaboration with different degrees of contributors, such as the core advocates, the advocates, the supporters, and the amplifiers. The results showed that a very small number of tweets created by the core advocates played significant roles due to their extensive adoption by other participants, while many other original tweets were never mentioned or retweeted in the network. This study disclosed the extensive proportion of amplifiers as 95.13% among the examined participants. The study findings suggest that creating core agenda tweets with high amplifiability might be critical for successful hashtag activism to attract like-minded masses as networked protesters. [ABSTRACT FROM AUTHOR]

A speech signal can provide various information about a speaker, such as their gender, age, accent, and emotional state. The gender of the speaker is the most salient piece of information contained in the speech signal and is directly or indirectly used in many applications. In this study, a new approach is proposed for recognizing the gender of the speaker based on the use of hybrid features created by stacking different types of features. For this purpose, four different features, namely Mel frequency cepstral coefficients (MFCC), Mel scaled power spectrogram (Mel Spectrogram), Chroma, Spectral contrast (Contrast), and Tonal Centroid (Tonnetz), and twelve hybrid features created by stacking these features were used. These features were applied to four different classifiers, two of which were based on traditional machine learning (KNN and LDA) while two were based on the deep learning approach (CNN and MLP), and the performance of each was evaluated separately. In the experiments conducted on the Turkish subset of the Common Voice dataset, it was observed that hybrid features, created by stacking different acoustic features, led to improvements in gender recognition accuracy ranging from 0.3 to 1.73%. [ABSTRACT FROM AUTHOR]

Cultural heritage is a fundamental part of the society's identity, and its conservation is of great relevance as it transcends time and memory. To minimize its deterioration, cultural heritage has traditionally undergone diverse preservation and maintenance treatments, and the attention of researchers to developing new and innovative methods for state diagnosis and protection treatments has been increasing in the recent decades. Despite extensive efforts in preservation, there remains a need for comprehensive and systematic mapping of scientific research to identify emerging trends and innovations in the field. To address this gap, in this study, a literature review using a bibliometric analysis and LDA methodology was conducted to systematically map scientific research outputs on cultural heritage conservation and protection. Data were retrieved from the Scopus database, and the annual publications, countries, most-cited publications, authors, institutions, and keywords have been comprehensively analyzed, leading to the detection of research trends and contributing to the existing knowledge in the field. The findings show an increasing number of studies in this field in the last decades, particularly since 2010. Italy, home to the largest number of UNESCO heritage sites, is the most prolific country. Most of the studies are related to metal, paper, and stone as substrates to be protected. Significant progress has been made in understanding the deterioration processes through precise diagnosis and the development of innovative treatments for protection. In this sense, the latest trends have been detected, such as the use of non-invasive techniques for diagnosis and the use of nanotechnology and nature-based treatments for conservation treatments. [ABSTRACT FROM AUTHOR]

The environmental impact of the golf industry has garnered increasing attention, emphasizing the need for sustainable practices in golf course management. This study aims to develop strategic frameworks that enable the golf industry to address global environmental challenges and integrate eco-friendly principles. Utilizing topic modeling, the research examines key areas such as pesticide use, waste management, and energy consumption within golf courses, and explores strategies for achieving carbon neutrality and developing sustainable golf wear and branding. The findings reveal specific management practices that significantly reduce environmental harm, including integrated pest management, waste reduction techniques, and the adoption of renewable energy sources. Case studies of leading golf clubs demonstrate successful implementation of carbon neutrality strategies. Furthermore, the research highlights the potential of eco-friendly golf wear to enhance brand credibility and meet consumer demands. These insights hold valuable implications for golf course managers, club operators, policymakers, and golfers, promoting a comprehensive approach to sustainability in the golf industry. The study's contributions extend beyond the immediate environmental challenges, offering a framework for long-term sustainability and competitive advantage. [ABSTRACT FROM AUTHOR]

In the current context of sluggish global economic recovery, widening regional disparities, and little room for policy error, the global employment situation is unprecedentedly severe. Therefore, research on sustainable employment is critical. In this study, we utilized a Latent Dirichlet Allocation (LDA) topic modeling approach to identify four central topics within the realm of sustainable employment: "Career Sustainability and Employee Development", "Socio-Economic Dynamics and Strategies for Sustainable Employment", "Study on Age, Health, and Sustainable Workforce in the Labor Market", and "Research on the Impact of Workplace Interventions and Psychological Health Support". The findings revealed the multidimensional, complex nature of sustainable employment issues. This study expands the theoretical horizon of sustainable employment and proposes multidimensional strategic suggestions for practical applications, including regularly assessing and updating organizations' leadership styles and employee development strategies, offering customized career development programs and skill training, and formulating inclusive employment policies and supportive workplace environments in order to enhance employee job satisfaction and overall well-being, thereby effectively improving the practical outcomes of sustainable employment and bringing about long-term stability and positive impacts on the labor market. These results provide essential theoretical and practical contributions for policymakers, organizational leaders, and anyone seeking sustainable employment. [ABSTRACT FROM AUTHOR]

Varietal volatile compounds are characteristic of each variety of grapes and come from the skins of the grapes. This work focuses on the development of a methodology for the analysis of free compounds in grapes from Trincadeira, Cabernet Sauvignon, Syrah, Castelão and Tinta Barroca from the 2021 and 2022 harvests, using HS-SPME-GC × GC-TOFMS. To achieve this purpose, a previous optimization step of sample preparation was implemented, with the optimized conditions being 4 g of grapes, 2 g of NaCl, and 2 mL of H2O. The extraction conditions were also optimized, and it was observed that performing the extraction for 40 min at 60 °C was the best for identifying more varietal compounds. The fiber used was a triple fiber of carboxen/divinylbenzene/polydimethylsiloxane (CAR/DVB/PDMS). In addition to the sample preparation, the analytical conditions were also optimized, enabling the adequate separation of analytes. Using the optimized methodology, it was possible to identify fifty-two free volatile compounds, including seventeen monoterpenes, twenty-eight sesquiterpenes, and seven C13-norisoprenoids. It was observed that in 2021, more free varietal volatile compounds were identifiable compared to 2022. According to the results obtained through a linear discriminant analysis (LDA), the differences in volatile varietal signature are observed both among different grape varieties and across different years. [ABSTRACT FROM AUTHOR]

In smart education, adaptive e-learning systems personalize the educational process by tailoring it to individual learning styles. Traditionally, identifying these styles relies on learners completing surveys and questionnaires, which can be tedious and may not reflect their true preferences. Additionally, this approach assumes that learning styles are fixed, leading to a cold-start problem when automatically identifying styles based on e-learning platform behaviors. To address these challenges, we propose a novel approach that annotates unlabeled student feedback using multi-layer topic modeling and implements the Felder–Silverman Learning Style Model (FSLSM) to identify learning styles automatically. Our method involves learners answering four FSLSM-based questions upon logging into the e-learning platform and providing personal information like age, gender, and cognitive characteristics, which are weighted using fuzzy logic. We then analyze learners' behaviors and activities using web usage mining techniques, classifying their learning sequences into specific styles with an advanced deep learning model. Additionally, we analyze textual feedback using latent Dirichlet allocation (LDA) for sentiment analysis to enhance the learning experience further. The experimental results demonstrate that our approach outperforms existing models in accurately detecting learning styles and improves the overall quality of personalized content delivery. [ABSTRACT FROM AUTHOR]

Stance information has a significant influence on market strategy, government policy, and public opinion. Users differ not only in their polarity but also in the degree to which they take a stand. The traditional classification of stances is quite simple and cannot fully depict the diversity of stances. At the same time, traditional approaches ignore user sentiment features when expressing their stances. As a result, this paper develops a multi-stance detection model by fusing sentiment features. First, a five-category stance indicator system is built based on the LDA model, then sentiment features are extracted from the reviews using the sentiment lexicon, and finally, stance detection is implemented using a hybrid neural network model. The experiment shows that the proposed method can classify stances into five categories and perform stance detection more accurately. [ABSTRACT FROM AUTHOR]

Scientific and technological innovation (STI) is an important internal driver of social and economic development. Reasonable evaluation of regional scientific and technological innovation (RSTI) capability helps discover shortcomings in the development of urban development and guides the allocation of scientific and technological resources and the formulation of policies to promote innovation. This paper analyzes new opportunities created by big data and artificial intelligence for the evaluation of RSTI capability, and based on this analysis, the collaborative evaluation schemes of multi-entity participation are investigated. In addition, considering the important value of unstructured data in evaluating STI, the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis method are employed to analyze the construction of an evaluation indicator system that integrates scientific and technological news data. To fully utilize the respective advantages of human experts and machine learning in the field of complex issue evaluation, this paper proposes an RSTI capability evaluation model based on AHP-SMO human-machine fusion. This study promotes the integration of science and technology and economy and has theoretical and practical significance. [ABSTRACT FROM AUTHOR]

As the esports industry experiences unprecedented growth, efforts to legitimize it as a recognized sport have intensified. This paper explores the transformative changes in China's esports landscape with focus on the aftermath of its official recognition at the 2023 Asian Games in Hangzhou. For this, this study employs a data-driven comparative approach to analyze both online news data and viewer comments on a video platform broadcasting the esports tournament during the Hangzhou Asian Games. For text data, it utilized text mining techniques of the Latent Dirichlet Allocation algorithm (LDA), and for conversion from image to text, it used through the Contrastive Language–Image Pretraining (CLIP) model. The findings reveal that the text and image data in online news emphasize the industry's positive anticipation of esports becoming a medal sport, but the public who watched esports events showed mixed expectations, doubts, or even negative sentiments about the reevaluation of esports. As evidenced in the paper, the public's divided stance towards esports might hinder the establishment of a solid consensus in developing the necessary infrastructure or policymaking. Thus, the transition for games to be recognized as an Olympic sport after their inclusion in the Asian Games demands a prolonged period and a comprehensive blend of economic, social, cultural, and educational policies. [ABSTRACT FROM AUTHOR]

Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares–discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis–linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia. [ABSTRACT FROM AUTHOR]

Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as "IF" and "THEN", and operators, such as ">" and "<", to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall. [ABSTRACT FROM AUTHOR]

The main goal of this research is to analyze the perception of service in public sector companies in the city of Bogota via Twitter and text mining to identify areas, problems, and topics aiming for quality service improvement. To achieve this objective, a structured method for data modeling is implemented based on the KDD methodology. Tweets from January to June 2022 related to the companies in the sector are processed, and a temporal analysis of the evolution of sentiment is performed based on the dictionaries Bing, AFINN, and NRC. Subsequently, the LDA algorithm (Latent Dirichlet Allocation algorithm) is used to visually identify the topics with the greatest negative impact reported by the users in each of the 6 months by adding the temporal dimension. The results revealed that, for Aqueduct (water supply service), the topic with the highest dissatisfaction is related to the "Water Tank Request" processes; for Enel (energy services) "Service Outages"; and for Vanti (gas services), "Case solution and request information". Temporal patterns of tweets, sentiments, and topics are also highlighted for the three companies. [ABSTRACT FROM AUTHOR]

Chatbots are AI-powered programs designed to replicate human conversation. They are capable of performing a wide range of tasks, including answering questions, offering directions, controlling smart home thermostats, and playing music, among other functions. ChatGPT is a popular AI-based chatbot that generates meaningful responses to queries, aiding people in learning. While some individuals support ChatGPT, others view it as a disruptive tool in the field of education. Discussions about this tool can be found across different social media platforms. Analyzing the sentiment of such social media data, which comprises people's opinions, is crucial for assessing public sentiment regarding the success and shortcomings of such tools. This study performs a sentiment analysis and topic modeling on ChatGPT-based tweets. ChatGPT-based tweets are the author's extracted tweets from Twitter using ChatGPT hashtags, where users share their reviews and opinions about ChatGPT, providing a reference to the thoughts expressed by users in their tweets. The Latent Dirichlet Allocation (LDA) approach is employed to identify the most frequently discussed topics in relation to ChatGPT tweets. For the sentiment analysis, a deep transformer-based Bidirectional Encoder Representations from Transformers (BERT) model with three dense layers of neural networks is proposed. Additionally, machine and deep learning models with fine-tuned parameters are utilized for a comparative analysis. Experimental results demonstrate the superior performance of the proposed BERT model, achieving an accuracy of 96.49%. [ABSTRACT FROM AUTHOR]

Featured Application: Fast ABO blood typing using Attenuated Total Reflectance—Fourier Transform Infrared Spectroscopy and artificial intelligence tools. Fourier Transform Infrared Spectroscopy (FTIR) provides valuable biochemical information for biomedical analysis. It aids in identifying cancerous tissues, diagnosing diseases like acute pancreatitis or Alzheimer's, and has applications in genomics, proteomics, and metabolomics. A combination of FTIR and chemometrics constitute an approach that shows promise in fields like biology, forensics, food quality control, and plant variety identification. This study aims to explore the feasibility of ATR-FTIR spectroscopy for identifying ABO-blood types using spectroscopic tools. We employ various classifying algorithms, including Linear Discriminant Analysis (LDA), Naïve Bayes Classifier (NBC), Principal Component Analysis (PCA), and combinations of these methods, to detect A and B antigens and determine the ABO blood type. The results show that these algorithms predict the blood type to a greater extent than random selection, although they do not match the precision of biochemical blood typing tools. Additionally, our findings suggest the higher sensitivity of the methodology in identifying B antigens compared to A antigens. [ABSTRACT FROM AUTHOR]

This study aims to explore the utilization of user-generated content for product improvement and decision-making processes. In the era of big data, the channels through which enterprises obtain user feedback information are transitioning from traditional methods to online platforms. The original data for this study were obtained from customer reviews of cordless hairdryers on JD.com. The specific process is as follows: First, we used the Python Requests package to crawl 20,157 initial comments. Subsequently, the initial data were cleaned, resulting in 1405 valid comments. Next, the cleaned and valid comments were segmented into Chinese words using the HanLP package. Finally, the Latent Dirichlet Allocation (LDA) method was applied for topic modeling. The visualization of the topic clustering was generated using pyLDAvis, and three optimal topics were identified. These topics were named "User Experience", "Product Evaluation", and "Product Features", respectively. Through data analysis and expert consultation, this study developed product design improvement strategies based on online reviews and verified the validity of the developed cordless hairdryer design index system through a questionnaire survey, providing practical references and innovative theoretical foundations for future product design assessments. [ABSTRACT FROM AUTHOR]

The proliferation of short texts resulting from the rapid advancements of social networks, online communication, and e-commerce has created a pressing need for short text classification in various applications. This paper presents a novel approach for short text classification, which combines a hierarchical heterogeneous graph with latent Dirichlet allocation (LDA) fusion. Our method first models the short text dataset as a hierarchical heterogeneous graph, which incorporates more syntactic and semantic information through a word graph, parts-of-speech (POS) tag graph, and entity graph. We then connected the representation of these three feature maps to derive a comprehensive feature vector for the text. Finally, we used the LDA topic model to adjust the feature weight, enhancing the effectiveness of short text extension. Our experiments demonstrated that our proposed approach has a promising performance in English short text classification, while in Chinese short text classification, although slightly inferior to the LDA + TF-IDF method, it still achieved promising results. [ABSTRACT FROM AUTHOR]

News impact analysis has become a common task conducted by finance researchers, which involves reading and selecting news articles based on themes and sentiments, pairing news events and relevant stocks, and measuring the impact of selected news on stock prices. To facilitate more efficient news selection, topic modeling can be applied to generate topics out of a large number of news documents. However, there is very limited existing literature comparing topic models in the context of finance-related news impact analysis. In this paper, we compare three state-of-the-art topic models, namely Latent Dirichlet allocation (LDA), Top2Vec, and BERTopic, in a defined scenario of news impact analysis on financial markets, where 38,240 news articles with an average length of 590 words are analyzed. A service-oriented framework for news impact analysis called "News Impact Analysis" (NIA) is advocated to leverage multiple topic models and provide an automated and seamless news impact analysis process for finance researchers. Experimental results have shown that BERTopic performed best in this scenario, with minimal data preprocessing, the highest coherence score, the best interpretability, and reasonable computing time. In addition, a finance researcher was able to conduct the entire news impact analysis process, which validated the feasibility and usability of the NIA framework. [ABSTRACT FROM AUTHOR]

Background: There is an immense debate about the factors that could limit the adoption of agroforestry systems. However, one of the most important is the generation of scientific information that supports the viability and benefits of the proposed techniques. Statistical analysis: This work used the Latent Dirichlet Allocation (LDA) modeling method to identify and interpret scientific information on topics in relation to existing categories in a set of documents. It also used the HJ-Biplot method to determine the relationship between the analyzed topics, taking into consideration the years under study. Results: A review of the literature was conducted in this study and a total of 9794 abstracts of scientific articles published between 1993 and 2022 were obtained. The United States, India, Brazil, the United Kingdom, and Germany were the five countries that published the largest number of studies about agroforestry, particularly soil organic carbon, which was the most studied case. The five more frequently studied topics were: soil organic carbon, adoption of agroforestry practices, biodiversity, climatic change global policies, and carbon and climatic change. Conclusion: the LDA and HJ-Biplot statistical methods are useful tools for determining topicality in text analysis in agroforestry and related topics. [ABSTRACT FROM AUTHOR]

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, they still face challenges in producing better performance with them because of the irrelevant features and high computational complexity. Selecting discriminative and relevant features to overcome the existing issues is crucial. In our proposed work, different feature selection algorithms have been studied to reduce the dimension of multiband feature space to improve MI task classification performance. In the procedure, we first decomposed the MI-based EEG signal into four sets of the narrowband signal. Then a common spatial pattern (CSP) approach was employed for each narrowband to extract and combine effective features, producing a high-dimensional feature vector. Three feature selection approaches, named correlation-based feature selection (CFS), minimum redundancy and maximum relevance (mRMR), and multi-subspace randomization and collaboration-based unsupervised feature selection (SRCFS), were used in this study to select the relevant and effective features for improving classification accuracy. Among them, the SRCFS feature selection approach demonstrated outstanding performance for MI classification compared to other schemes. The SRCFS is based on the multiple k-nearest neighbour graphs method for learning feature weight based on the Laplacian score and then discarding the irrelevant features based on the weight value, reducing the feature dimension. Finally, the selected features are fed into the support vector machines (SVM), linear discriminative analysis (LDA), and multi-layer perceptron (MLP) for classification. The proposed model is evaluated with two benchmark datasets, namely BCI Competition III dataset IVA and dataset IIIB, which are publicly available and mainly used to recognize the MI tasks. The LDA classifier with the SRCFS feature selection algorithm exhibits better performance. It proves the superiority of our proposed study compared to the other state-of-the-art BCI-based MI task classification systems. [ABSTRACT FROM AUTHOR]

Topic extraction and evolution analysis became a research hotspot in the academic community due to its ability to reveal the development trend of a certain field and discover the evolution law of topic content in different development stages of the field. However, current research methods still face challenges, such as inaccurate topic recognition and unclear evolution paths, which can seriously compromise the comprehensiveness and accuracy of the analysis. To address the problem, the paper proposes a topic evolution path recognition method based on the LDA2vec symmetry model. Under given conditions, both the LDA and Word2vec used in the model conform to the structural symmetry of their datasets in high-dimensional space, and the fused LDA2vec method improves the accuracy of the analysis results. Firstly, we recognize the topics based on the LDA model, which uses Gibbs symmetric sampling and obeys the symmetric Dirichlet distribution to ensure data convergence. Secondly, Word2vec is used to learn the contextual information of the topic words in the document collection, and the words in the corpus are projected as vectors in the high-dimensional space so that the computed pairs of words with similar semantics have symmetry in the hyperplane of the high-dimensional space. Subsequently, the word vector is used as a weight, and the LDA topic word probability value is weighted to generate a new topic vector. Thirdly, the vector similarity index is employed to calculate the semantic similarity among topics at adjacent stages, and evolution paths that directly reflect the topic relationships are constructed. Finally, an empirical study is conducted in the field of data security to demonstrate the effectiveness of the proposed approach for topic evolution analysis. The results show that the proposed approach can accurately recognize the topic content and construct clear evolution paths, which contribute to the comprehensive and accurate analysis of topic evolution in a specific research field. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explores the use of biometric analysis to identify the sex of Bonelli's eagles, a bird species with reverse sexual dimorphism where females are usually larger than males. By using linear discriminant analysis of biometric variables, we were able to obtain equations that accurately distinguished between male and female eagles, using just two measurements. The study included 137 Bonelli's eagles, 82 nestlings and 55 adults, sampled in eastern Spain from 2015 to 2022. The validation procedure reduced the number of variables used, increasing the accuracy of sexing and saving time. Results showed that the lateral tarsus length and dorso-ventral tarsus length measurements were the most effective in distinguishing between male and female eagles of all age classes, while other variables showed some overlap between sexes. This non-invasive method has multiple applications, including estimation of sex ratio for further studies on population dynamics and extinction risk assessments, which could contribute to the conservation of this endangered species. Biometric analysis allows the sexing of most vertebrates, particularly birds. Birds of prey, and, especially, the Bonelli's eagle (Aquila fasciata), show reverse sexual dimorphism (i.e., females are usually larger than males). In contrast to blood sampling, the use of morphometrics allows sex determination using a non-invasive method, and, therefore, it facilitates fieldwork. By means of a linear discriminant analysis of biometric variables, we obtained different equations that allow the sexing of nestlings and adult Bonelli's eagles. We sampled 137 Bonelli's eagles, 82 nestlings and 55 adults in eastern Spain during the period 2015–2022. The sexes obtained after linear discriminant analysis were compared with their molecular sexing. The validation procedure of the linear discriminant equations facilitated the reduction of the number of variables used and, consequently, optimised working time and sexing accuracy. After validation, some equations showed a 100% sexing efficiency for Bonelli's eagles, particularly for adults. Our results showed that the variables with smaller overlap between the sexes were the lateral tarsus length and dorso-ventral tarsus length, particularly in nestlings. The rest of the variables showed some overlap between the sexes in both age classes. The results we obtained enable the sexing of juvenile and adult Bonelli's eagles in the field using just these two measurements. Hence, this is an easy, accurate, quick and non-invasive method with multiple applications, including in studies on population dynamics, survival analysis or extinction risk assessments, which, ultimately, could contribute to the improvement of the conservation status of this endangered species. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters. [ABSTRACT FROM AUTHOR]

We investigated the effect of land use based on the dominant phytoplankton and physical-chemical variables in the different areas of the Tigris River, within the city of Mosul. Agricultural and urban activities have a significant impact on the water quality of the river. Regardless of physical and chemical variables, phytoplankton act as a bio-indicator of water quality due to their fast and sensitive response to changes in the environment. Our research was conducted in the Tigris River within the city of Mosul by examining the phytoplankton species and the physical-chemical variables at 16 sites during each vegetation period. Point and non-point source pollutants have affected the Tigris River within the city of Mosul, getting into the river from upstream through agricultural activities and by urban activities in the middle section of the city, respectively from both banks. Based on our results, we observed the highest phytoplankton abundance during the October sampling periods, while the lowest occurred during the July sampling period, which was associated with maximum water temperature and absence of rain. According to our study, land use (e.g., agricultural, and urban activities) greatly affected the dominant phytoplankton species and physical-chemical variables of the Tigris River. Oscillatoria sp. dominated all seasons in the agricultural region, while at the same time, we observed an increase in the number of phytoplankton species caused by the nutrient availability upstream on the river. The effects of climate have very significant and characteristic effects in this area, which basically determine the community of organisms and the water quality; the effects resulting from anthropogenic activity significantly modify this. Based on our investigation, in the part of the Tigris River connected to Mosul, we found a clear connection between the pollution caused and the effects of different land uses, through the examination of the algal community and physical-chemical variables in different periods of the year. [ABSTRACT FROM AUTHOR]

In this study, the author collected tweets about ChatGPT, an innovative AI chatbot, in the first month after its launch. A total of 233,914 English tweets were analyzed using the latent Dirichlet allocation (LDA) topic modeling algorithm to answer the question "what can ChatGPT do?". The results revealed three general topics: news, technology, and reactions. The author also identified five functional domains: creative writing, essay writing, prompt writing, code writing, and answering questions. The analysis also found that ChatGPT has the potential to impact technologies and humans in both positive and negative ways. In conclusion, the author outlines four key issues that need to be addressed as a result of this AI advancement: the evolution of jobs, a new technological landscape, the quest for artificial general intelligence, and the progress-ethics conundrum. [ABSTRACT FROM AUTHOR]

Question-asking is a critical aspect of human communications. Yet, little is known about the reasons that lead people to ask questions, which questions are considered better than others, or what cognitive mechanisms allow the ability to ask informative questions. Here, we take a first step towards investigating human question-asking. We do so by an exploratory data-driven analysis of the questions asked by Akinator, a popular online game of a genie who asks questions to guess the character that the user is thinking of. We propose that the Akinator's question-asking process may be viewed as a reflection of how humans ask questions. We conduct an exploratory data analysis to examine different strategies for the Akinator's question-asking process, ranging from mathematical algorithms to gamification-based considerations, by analyzing complete games and individual questions. Furthermore, we use topic-modelling techniques to explore the topics of the Akinator's inquiries and map similar questions into clusters. Overall, we find surprising aspects of the specificity and types of questions generated by the Akinator game, that may be driven by the gamification characteristics of the game. In addition, we find coherent topics that the Akinator retrieves from when generating questions. Our results highlight commonalities in the strategies for question-asking used by people and by the Akinator. [ABSTRACT FROM AUTHOR]

A biometric authentication system is more convenient and secure than graphical or textual passwords when accessing information systems. Unfortunately, biometric authentication systems have the disadvantage of being susceptible to spoofing attacks. Authentication schemes based on biometrics, including face recognition, are susceptible to spoofing. This paper proposes an image encryption scheme to counter spoofing attacks by integrating it into the pipeline of Linear Discriminant Analysis (LDA) based face recognition. The encryption scheme uses XOR pixels substitution and cellular automata for scrambling. A single key is used to encrypt the training and testing datasets in LDA face recognition system. For added security, the encryption step requires input images of faces to be encrypted with the correct key before the system can recognize the images. An LDA face recognition scheme based on random forest classifiers has achieved 96.25% accuracy on ORL dataset in classifying encrypted test face images. In a test where original test face images were not encrypted with keys used for encrypted feature databases, the system achieved 8.75% accuracy only showing it is capable of resisting spoofing attacks. [ABSTRACT FROM AUTHOR]

In this study, a multivariate analysis combined with near-infrared (NIR) spectroscopy was employed to classify intact grape berries based on the rootstock x cover crops combination. NIR spectra were collected in diffuse reflection mode using a TANGO FT-NIR spectrometer (Bruker, Germany) with 8 cm−1 resolution and 64 scans in the wave number range of 4000–10,000 cm−1. The chemometric analyses were performed with the statistical software R version 4.2.0 (2022-04-22). Elimination of uninformative variables was accomplished with a PCA and a genetic algorithm (GA). The discrimination performance of a linear discriminant analysis (LDA) model was not enhanced with either a PCA- or a GA-based selection. A multiclass classification model was built with an artificial neural network (ANN). The best fit multiclass classification model on test data was obtained with the GA-ANN model that gave a classification accuracy of close to 80% for samples belonging to the four classes. These results demonstrate that NIR spectroscopy could be used as a rapid method for the classification of berries based on their rootstock x cover-crops combination. [ABSTRACT FROM AUTHOR]

The paper presents a new approach to designing the stator of a multistage centrifugal pump, which has a simple structure, satisfactory operating parameters, and is easy to manufacture. The stator is distinguished by its lack of a classic diffuser part, with the shape of its crossover being similar to a segment of a spherical surface. This solution was patented in 1989, but to date it is not well known and practically not used. The paper attempts to identify the flow phenomena in such a stator and also to examine the impact of the design features of the stator on the pump's operating parameters. Both experimental methods and numerical modelling were used in the research. [ABSTRACT FROM AUTHOR]

The impact of the COVID-19 pandemic on fresh food e-commerce has led to a loss of consumers, and "e-commerce temperature" is seen as an important means of alleviating consumer dissatisfaction and retaining consumers. To explore the connotation and effect of it, and to break through possible "comfort" bottlenecks, we used online reviews of the Jingdong fresh food platform as research data, mined the characteristics of "e-commerce temperature" with the help of the LDA topic model, and evaluated the mechanism of "e-commerce temperature" on consumer satisfaction during the pandemic by using quasi-natural experiments and Word2vec-based sentiment analysis. The results show that "e-commerce temperature" has five connotations of logistics commitment, humanized delivery, health pledge, pandemic perseverance, and consumer care, which can effectively mitigate the loss of consumer satisfaction. Interestingly, we found that the "e-commerce temperature" has a limited "comfort" effect. Additionally, further social network analysis shows that the bottleneck is mainly due to the consumers' psychological gaps when comparing the usual e-commerce services, and cretailers can repair them through financial compensation and spiritual solace. The study explores e-commerce service quality at different pandemic stages with the help of text mining techniques, enriches the theory of e-commerce research, and alleviates the Hawthorne bias in traditional empirical studies. This study also provides a reference for e-retailers to improve service quality and respond to emergencies in a changing post-pandemic era. [ABSTRACT FROM AUTHOR]

Recently, food safety and cold chain food have been closely related to the epidemic. The party and the state have intensified efforts to solve food safety problems and prevent possible epidemic risks. China has issued a series of policies and plans to strengthen food safety supervision to improve the food safety policy system. To our knowledge, little work has studied policy problems of food safety with in-depth quantitative analysis for an extended period. In accordance with the different national policies and regulations for food safety, this paper fills the gap by analyzing the policies and comparing the central and local policies issued in China from 2007–2022. In addition, the Latent Dirichlet Allocation (LDA) topic model and K-Means clustering model are constructed to analyze the content of food safety policies and identify hot topics. Finally, a quantitative analysis of China's food safety policies is made from four aspects: the number of policy release years, the distribution area, the range of action, and the affiliated institutions. The results show that: (a) there is a partial surge in food safety policies issued in 2007, 2011, and 2017; (b) the local food safety policy has a high inheritance to the central policy content, and the trends of annual publication number are highly consistent; (c) the innovation of different policy contents in the region have their own characteristics; (d) the proportion of compulsory and capacity policies is much more significant than that of other types of policies. This paper provides some novel insights into food safety policies. [ABSTRACT FROM AUTHOR]

This paper assesses a custom single-type electronic nose (eNose) applied to differentiate the complex aromas generated by the caffeinated and decaffeinated versions of one encapsulated espresso coffee mixture type. The eNose used is composed of 16 single-type (identical) metal–oxide semiconductor (MOX) gas sensors based on microelectromechanical system (MEMS). This eNose proposal takes advantage of the small but inherent sensing variability of MOX gas sensors in order to provide a multisensorial description of volatiles or aromas. Results have shown that the information provided with this eNose processed using LDA is able to successfully discriminate the complex aromas of one caffeinated and decaffeinated encapsulated espresso coffee type. [ABSTRACT FROM AUTHOR]

The 1H-NMR carbohydrates profiling was used to discriminate fruits from Rosaceae family in terms of botanical origin and harvest year. The classification was possible by application of multivariate data analysis, such as principal component analysis (PCA), linear discriminant analysis (LDA) and Pearson analysis. Prior, a heat map was created based on 1H-NMR signals which offered an overview of the content of individual carbohydrates in plum, apricot, cherry and sour cherry, highlighting the similarities. Although, the PCA results were almost satisfactory, based only on carbohydrates signals, the LDA reached 94.39% and 100% classification of fruits according to their botanical origin and growing season, respectively. Additionally, a potential association with the relevant climatic data was explored by applying the Pearson analysis. These findings are intended to create an efficient NMR-based solution capable of differentiating fruit juices based on their basic sugar profile. [ABSTRACT FROM AUTHOR]

Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) and topic modeling based on BERT transformer, to extract meaningful insights in the Italian narration of COVID-19 pandemic. In particular, the main focus was the characterization of Post-acute Sequelae of COVID-19, (i.e., PASC) writings as opposed to writings by health professionals and general reflections on COVID-19, (i.e., non-PASC) writings, modeled as a semi-supervised task. The results show that the BERTopic-based approach outperforms the LDA-base approach by grouping in the same cluster the 97.26% of analyzed documents, and reaching an overall accuracy of 91.97%. [ABSTRACT FROM AUTHOR]

Current applications of the Internet of Everything (IOE) tend to ignore the components of people, processes, and data. In the current study, we propose applying the IOE to design, the core of which comprises people, processes, and data. Evidence of the positive effects of the IOE on design indirectly confirms the value of these three components. The selected case study was poster designs advertising personalized travel. We reported the process of how the system was created, followed by an actual simulation with the personalized poster design for the tourism promotion for the city of Tainan, Taiwan. This study created an IOE framework and applied relevant algorithms to collect data to target design issues. Data were collected, converted, and cleaned through multiple physical objects, internet resources, and output keywords, which were then provided to three designers with various backgrounds and styles. Posters were designed based on the keywords identified. As the targeted audience, 124 respondents were recruited to participate in a survey on the finished posters. Analysis of this feedback demonstrated the validity of the proposed framework. [ABSTRACT FROM AUTHOR]

Heart disease is a danger to people's health because of its prevalence and high mortality risk. Predicting cardiac disease early using a few simple physical indications collected from a routine physical examination has become difficult. Clinically, it is critical and sensitive for the signs of heart disease for accurate forecasts and concrete steps for future diagnosis. The manual analysis and prediction of a massive volume of data are challenging and time-consuming. In this paper, a unique heart disease prediction model is proposed to predict heart disease correctly and rapidly using a variety of bodily signs. A heart disease prediction algorithm based on the analysis of the predictive models' classification performance on combined datasets and the train-test split technique is presented. Finally, the proposed technique's training results are compared with the previous works. For the Cleveland, Switzerland, Hungarian, and Long Beach VA heart disease datasets, accuracy, precision, recall, F1-score, and ROC-AUC curves are used as the performance indicators. The analytical outcomes for Random Forest Classifiers (RFC) of the combined heart disease datasets are F1-score 100%, accuracy 100%, precision 100%, recall 100%, and the ROC-AUC 100%. The Decision Tree Classifiers for pooled heart disease datasets are F1-score 100%, accuracy 98.80%, precision 98%, recall 99%, ROC-AUC 99%, and for RFC and Gradient Boosting Classifiers (GBC), the ROC-AUC gives 100% performance. The performances of the machine learning algorithms are improved by using five-fold cross validation. Again, the Stacking CV Classifier is also used to improve the performances of the individual machine learning algorithms by combining two and three techniques together. In this paper, several reduction methods are incorporated. It is found that the accuracy of the RFC classification algorithm is high. Moreover, the developed method is efficient and reliable for predicting heart disease. [ABSTRACT FROM AUTHOR]

This work objective is to generate an HJ-biplot representation for the content analysis obtained by latent Dirichlet assignment (LDA) of the headlines of three Spanish newspapers in their web versions referring to the topic of the pandemic caused by the SARS-CoV-2 virus (COVID-19) with more than 500 million affected and almost six million deaths to date. The HJ-biplot is used to give an extra analytical boost to the model, it is an easy-to-interpret multivariate technique which does not require in-depth knowledge of statistics, allows capturing the relationship between the topics about the COVID-19 news and the three digital newspapers, and it compares them with LDAvis and heatmap representations, the HJ-biplot provides a better representation and visualization, allowing us to analyze the relationship between each newspaper analyzed (column markers represented by vectors) and the 14 topics obtained from the LDA model (row markers represented by points) represented in the plane with the greatest informative capacity. It is concluded that the newspapers El Mundo and 20 M present greater homogeneity between the topics published during the pandemic, while El País presents topics that are less related to the other two newspapers, highlighting topics such as t_12 (Government_Madrid) and t_13 (Government_millions). [ABSTRACT FROM AUTHOR]

Numerical prediction of discharge parameters allows design of a pressure-swirl atomizer in a fast and cheap manner, yet it must provide reliable results for a wide range of geometries and operating regimes. Many authors have used different numerical setups for similar cases and often concluded opposite suggestions on numerical setup. This paper compares 2D (two-dimensional) axisymmetric, 3D (three-dimensional) periodic and full 3D numerical models used for estimation of the internal flow characteristics of a pressure-swirl atomizer. The computed results are compared with experimental data in terms of spray cone angle, discharge coefficient (CD), internal air-core dimensions, and velocity profiles. The three-component velocity was experimentally measured using a Laser Doppler Anemometry in a scaled transparent model of the atomizer. The internal air-core was visualized by a high-speed camera with backlit illumination. Tested conditions covered a wide range of the Reynolds numbers within the inlet ports, Re = 1000, 2000, 4000. The flow was treated as both steady and transient flow. The numerical solver used laminar and several turbulence models, represented by k-ε and k-ω models, Reynolds Stress model (RSM) and Large Eddy Simulation (LES). The laminar solver was capable of closely predicting the CD, air-core dimensions and velocity profiles compared with the experimental results in both 2D and 3D simulations. The LES performed similarly to the laminar solver for low Re and was slightly superior for Re = 4000. The two-equation models were sensitive to proper solving of the near wall flow and were not accurate for low Re. Surprisingly, the RSM produced the worst results. [ABSTRACT FROM AUTHOR]

The fast differentiation and classification of herb samples are complicated processes due to the presence of many various chemical compounds. Traditionally, separation techniques have been employed for the identification and quantification of compounds present in different plant matrices, but they are tedious, time-consuming and destructive. Thus, a non-targeted approach would be specifically advantageous for this purpose. In the present study, spectroscopy in the visible and near-infrared range and pattern recognition techniques, including the principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), super k-nearest neighbor (SKNN) and support vector machine (SVM) techniques, were applied to develop classification models that enabled the discrimination of various commercial dried herbs, including mint, linden, nettle, sage and chamomile. The classification error rates in the validation data were below 10% for all the classification methods, except for SKNN. The results obtained confirm that spectroscopy and pattern recognition methods constitute a good non-destructive tool for the rapid identification of herb species that can be used in routine quality control by the pharmaceutical industry, as well as herbal suppliers, to avoid mislabeling. [ABSTRACT FROM AUTHOR]

During natural disasters, social media can provide real time or rapid disaster, perception information to help government managers carry out disaster response efforts efficiently. Therefore, it is of great significance to mine social media information accurately. In contrast to previous studies, this study proposes a multimodal data classification model for mining social media information. Using the model, the study employs Late Dirichlet Allocation (LDA) to identify subject information from multimodal data, then, the multimodal data is analyzed by bidirectional encoder representation from transformers (Bert) and visual geometry group 16 (Vgg-16). Text and image data are classified separately, resulting in real mining of topic information during disasters. This study uses Weibo data during the 2021 Henan heavy storm as the research object. Comparing the data with previous experiment results, this study proposes a model that can classify natural disaster topics more accurately. The accuracy of this study is 0.93. Compared with a topic-based event classification model KGE-MMSLDA, the accuracy of this study is improved by 12%. This study results in a real-time understanding of different themed natural disasters to help make informed decisions. [ABSTRACT FROM AUTHOR]

Vegetables are an important element in people's dietary structure, and the price fluctuation of vegetables has attracted more and more attention. The disturbance sources of vegetable price fluctuations are characterized by uncertain risks, environmental complexity, nonlinearity, self-organization and mutation. Analyzing the disturbance sources that affect vegetable price fluctuation is helpful to the establishment of early warning and regulation mechanisms of vegetable price risk. To address the problem that existing studies have not comprehensively and objectively clarified the disturbance sources of vegetable price fluctuations, this paper proposes a method of combining the LDA (Latent Dirichlet Allocation) topic model with grounded theory, constructs a system of vegetable price volatility disturbance source indicators and relationship matrix by improved conceptual lattice-weighted cluster method, obtains 23 disturbance sources indicators affecting vegetable price fluctuations in four aspects of supply, demand, natural environment and economic policy environment, and identifies six key factors through calculation and analysis. Through the research of this paper, a system of disturbance source indicators affecting vegetable price fluctuations is constructed, the internal connection of many disturbance sources of vegetable price fluctuations in a complex and uncertain environment is clarified, and key influencing factors are selected, thus facilitating the establishment of vegetable price risk warning models and regulation mechanisms. [ABSTRACT FROM AUTHOR]

Induction motors (IMs) have been extensively used for driving a wide variety of processes in several industries. Their excellent performance, capabilities and robustness explain their extensive use in several industrial applications. However, despite their robustness, IMs are susceptible to failure, with broken rotor bars (BRB) being one of the potential faults. These types of faults usually occur due to the high current amplitude flowing in the bars during the starting transient. Currently, soft-starters have been used in order to reduce the negative effects and stresses developed during the starting. However, the addition of these devices makes the fault diagnosis a complex and sometimes erratic task, since the typical fault-related patterns evolutions are usually irregular, depending on particular aspects that may change according to the technology implemented by the soft-starter. This paper proposes a novel methodology for the automatic detection of BRB in IMs under the influence of soft-starters. The proposal relies on the combined analysis of current and stray flux signals by means of suitable indicators proposed here, and their fusion through a linear discriminant analysis (LDA). Finally, the LDA output is used to train a feed-forward neural network (FFNN) to automatically detect the severity of the failure, namely: a healthy motor, one broken rotor bar, and two broken rotor bars. The proposal is validated under a testbench consisting of a kinematic chain driven by a 1.1 kW IM and using four different models of soft-starters. The obtained results demonstrate the capabilities of the proposal, obtaining a correct classification rate (94.4% for the worst case). [ABSTRACT FROM AUTHOR]