Processing Tools Research Articles

Race and Ethnicity and Clinician Linguistic Expressions of Doubt in Hospital Admission Notes

Stigmatizing language in electronic health records (EHRs) can generate or reinforce negative stereotypes about patients from minoritized groups and erode their trust and experience. However, less is known about the use of such language to cast doubt on patient clinical history in hospital settings. To compare the prevalence of language expressing doubt about patient clinical history (eg, experiences and symptoms) in hospital admission notes by patient race and ethnicity. In this cohort study, natural language processing tools were applied to analyze 54 936 admission notes from 1249 clinicians about patients aged 18 years or older at admission and hospitalized at an academic health system in the Northeast US between January 1, 2018, and February 28, 2023. Data were analyzed from September 1, 2022, to July 31, 2023. Patient race and ethnicity (non-Hispanic Black, non-Hispanic White, and racial and ethnic minoritized groups excluding non-Hispanic Black [includes multiple racial and ethnic groups, such as Hispanic and Asian]) as recorded in the EHR based on self-report or registrar determination. Binary indicator for at least 1 term casting doubt on patient clinical history was defined using epistemic stance, a linguistic construct expressing a writer's degree of certainty in information. Terms were manually validated via iterative review of notes by the study team. Among 56 325 admission notes (mean [SD] age of patients, 55.9 [19.0] years; 30 913 notes among female patients [54.9%]; 25 649 notes among non-Hispanic Black patients [45.5%], 26 442 notes among non-Hispanic White patients [46.9%], and 2985 notes among members of racial and ethnic minoritized groups excluding non-Hispanic Black patients [5.3%]), we analyzed 54 936 admission notes that had no missing data. Among all analyzed admission notes, 39 023 notes (71.0%) contained doubt language. Notes written about non-Hispanic Black patients had increased odds of containing at least 1 word or phrase of doubt (adjusted odds ratio, 1.21; 95% CI, 1.14-1.28; P < .001) compared with notes among non-Hispanic White patients. Compared with notes among non-Hispanic White patients, notes written about members of racial and ethnic minoritized groups excluding non-Hispanic Black patients had similar odds of containing at least 1 term of doubt. In this study, language casting doubt on patient clinical history was more likely to be found in notes of non-Hispanic Black patients. These results suggest that with the implementation of policies allowing patients full access to their health records, examining clinical documentation standards may be associated with improved patient experience.

Medición de la pobreza multidimensional en México mediante un análisis bibliométrico y de ecuaciones estructurales

Objective. Multidimensional poverty in Mexico was measured through a bibliometric and structural equation analysis. Design/Methodology/Approach. We used the technique developed by the National Council for the Evaluation of Social Development Policy (CONEVAL, in Spanish) in Mexico, based on information from the National Household Income and Expenditure Survey (ENIGH, in Spanish). The Smart PLS software also used the partial least squares (PLS) technique. Regarding the bibliometric part, a descriptive analysis was performed using the Scopus database and VOSviewer as a processing tool. Results/Discussion. The results showed that basic housing services, access to food, quality, housing spaces, and income significantly impact multidimensional poverty. The PLS model showed adequate predictive superiority and goodness of fit. From the bibliometric point of view, there was clear interest on the part of the academic and scientific community in developing knowledge in this field. Conclusions. The effectiveness of the PLS model for measuring multidimensional poverty in Mexico is demonstrated, and several determinants are highlighted. Future studies are recommended to improve social policies and poverty reduction strategies. Originality/Value. The study provides a novel approach by using the PLS model to measure multidimensional poverty in Mexico. It applies a robust exploratory methodology that can be replicated in similar contexts and is supported by bibliometric contrast.

Prospect for Fine and Coarse Coal Waste Deployment for a Constructed Technosol and Eragrostis Tef Growth

The aim of this study was to evaluate soil properties and Eragrostis tef (teff) growth on Technosols produced from coarse and fine coal wastes from Moatize Mine, Mozambique. The experiment was performed in triplicate in 30 L containers filled with different substrate conditions, composed of fine coal waste, coarse coal waste, agricultural soil, and sewage sludge as an organic matter source. The soil analyses included bulk density, available water capacity, permeability, and fertility. Plant growth was monitored for biomass production and plant tissue composition. All the substrates presented a good available water capacity and a proper drainage condition. Regarding soil fertility, there were shortages of potassium and boron in the substrates composed exclusively of coal wastes, which was reflected in the composition of the plant tissue. Even so, plant growth was statistically equivalent to the control in all conditions, except for the substrate produced exclusively with fine coal waste and sewage sludge, which presented a better performance. Technosols are an alternative for reducing the final mine waste volume, and Eragrostis tef is used as a means for land use after the mining process, with social gains, and as a tool in an ecological process for restoring coal mining sites.

RANCANG BANGUN ALAT PEMOTONG DAUN BAWANG BERBASIS INTERNET OF THINGS (IOT)

The utilization of Internet of Things (IoT) technology in the agriculture sector continues to evolve, including in the automation of agricultural processing tools. This research aims to design and develop an IoT-based scallion cutting tool integrated with the Blynk application, which functions to control and manage the device in real-time. This tool is equipped with key components such as a cutting motor, sensors, and a microcontroller, all controlled via the IoT network. The Blynk application allows users to adjust cutting parameters, such as motor speed, and receive information about the tool's status. Testing has shown that this tool can operate automatically according to settings provided through the Blynk app, producing consistent scallion cuts that meet the requirements. The implementation of this technology is expected to enhance efficiency, reduce manual labor and time, and improve the productivity and quality of scallion cutting. With the integration of IoT and the Blynk application, this scallion cutting tool offers an innovative solution to support the modernization of the agricultural industry, particularly in scallion processin.

From greatest simplicity to full power

AbstractWhile language processing services are key assets for the science and technology of language, the possible ways under which they may be made available to the widest range of their end users are critical to support an Open Science policy for this scientific domain. Although providing such processing services under some web-based interface, at large, offers itself as an immediate and cogent response to that challenge, turning this view into an effective access to language processing services is an undertaking deserving a clear conceptual direction and a corresponding robust empirical validation. Based on an extensive overview of major undertakings towards making language processing tools available and on the design principles worked out and implemented in the PORTULAN CLARIN infrastructure, in this paper we advocate for a Research-Infrastructure-as-a-Service (RIaaS) model. This model unleashes accessibility to language processing services in as many web-based interface modalities as the current stage of technological development permits to support, in order to serve as many types of end users as possible, from IT developers to Digital Humanities researchers, and including citizen scientists, teachers, students and digital artists among many others.

ICT in biology education: teachers’ readiness and implementation challenges in the context of Bangladesh

Information and Communication Technology (ICT) is commonly employed in biology classes to facilitate students' comprehensive comprehension of biological processes and enable them to see living phenomena. The purpose of the study was to explore the use of ICT in enhancing the teaching and learning process of biology in the secondary schools of Bangladesh. In our study, sequential explanatory mixed method design has been followed. Data was collected from purposively selected ten secondary schools of Dhaka city. The study reveals that teachers have a positive attitude towards incorporating ICT in biology lessons and possess operational understanding of data processing tools and online browsing. Nevertheless, it also emphasizes the fact that teachers are devoid of personal computers in their houses and have restricted availability to school computers. Furthermore, the existing curriculum and teachers' guides lack adequate instructions for integrating ICT into biology lectures. The study also suggests that the process of developing ICT-based curriculum is complicated, which deters teachers from incorporating ICT into their classes. Furthermore, there is a limited availability of educational materials related to ICT in the Bangla language. This study recommends continuous in-servicing training on ICT to teachers, interest free loan to teachers for purchasing laptops, developing interactive biology educational ICT contents in Bangla and include specific instructions in the curriculum and teachers’ guide on how to use ICT in biology classes. These findings have important implications for policy makers, educators, and researchers who are dedicated to expanding the use of ICT in education.

Vispro improves imaging analysis for Visium spatial transcriptomics.

Spatial transcriptomics (ST) is a cutting-edge technology that enables the comprehensive analysis of gene expression while preserving the spatial context of tissues. Within this framework, histological images serve a crucial role, providing spatially cohesive information that is often challenging to capture through gene expression alone. However, the imaging process in ST data presents inherent challenges, such as variability in image quality, artifacts from fiducial markers, and difficulty in distinguishing tissue regions. These challenges can significantly impair the accuracy and effectiveness of downstream analyses. To address these limitations, we developed Vispro, an end-to-end, fully automated image processing tool tailored for ST data. Vispro integrates four key modules: fiducial marker detection, marker removal and image restoration, tissue region detection, and segmentation of disconnected tissue areas. These modules systematically enhance the quality of ST images, reducing artifacts and improving tissue segmentation accuracy. Furthermore, by improving the integrity of image data, we demonstrate that Vispro facilitates downstream analyses such as cell segmentation and image registration, empowering researchers to extract more detailed and accurate biological insights from complex tissue architectures.

«ОПТИКА» БӨЛІМІ БОЙЫНША ЭЛЕКТРОНДЫҚ ОҚУЛЫҚТАРДЫҢ ЗАМАНУИ БІЛІМ БЕРУ ҚҰРАЛЫ РЕТІНДЕ РӨЛІ

The article examines the effectiveness and role of the use of electronic textbooks in the education system. And, including in the section «Optics», optical phenomena and patterns are taken as a basis. The creation of electronic textbooks is one of the most pressing problems of the educational process today. The expansion of the use of modern information technologies has led to the need to develop electronic learning tools that increase the effectiveness of self-learning and the quality of learning for students. The use of electronic means in the learning process is effective for both the teacher and the student. The use of electronic learning tools in the educational process does not contradict the traditional teaching method, but rather complements the use of its achievements. Comparison parameters are also considered: page and rotation; reader's movement through the book; plane and depth; book block; personal appearance of the book; text; illustration; tactile sensations; reliability of information storage and accessible information update; one can say about the pros and cons of this and other types of books. The advantages of an electronic textbook in terms of printing are accompanied only by sound effects associated with its large information capacity and ease of use. With all the convenience of the e-book format, the reader may want to see it directly on the site if he does not want to download it, or, as in a bookstore, first view the publication and then decide whether to download it or not. The main disadvantages of the electronic textbook are due to its loss of personal artistic appearance. Currently, there are many tools for creating electronic textbooks. For example, the possibilities of the program SunRav BookEditor, SunRav BookReader, TurboSite, Flipsnack, EdApp, Canva, etc. are considered. In the article, we will define the role of analyzing the digital literacy of students using an electronic textbook. Examples of electronic forms of a physics textbook for grades 7-9 can be given.

Age Group Differences in Social Media Language Use: Identity Representation via Emojis and Abbreviations

This paper aims to analyse how age groups impact the use of social media language (SL) in online textual conversations, focusing on the type, polarity, and subjectivity of SL to explore identity representation across different age groups. During data collection in 2023, there were 4.76 billion social media users worldwide; hence, understanding the use of SL is essential for analysing how individuals express their emotions and identities online. SL consists of emoticons/emojis, abbreviations, and mixed language in textual conversations. For this study, a qualitative approach was used, with 46 participants from three age groups: 25-34, 35-44, and 45-54 engaging in mock group conversations for one hour using WhatsApp. The participants were separated into six group conversations. The findings show that the older age groups (35-44 and 45-54) prefer to discuss technical or professional topics, whereas the younger age groups (25-34) are more interested in games, food, entertainment, and fun. Furthermore, for the age group 25-34 there is a high usage of emoticons/emojis, whereas the age group 45-54 uses them the least. These findings indicate that age groups have a significant influence on the use of SL and topic preferences in textual conversations, providing insights that will be used to develop a Natural Language Processing (NLP) tool for online identity classification. This study enhances human-computer interaction by investigating how different age groups use SL in digital environments.

Analyzing hope speech from psycholinguistic and emotional perspectives

Hope is a vital coping mechanism, enabling individuals to effectively confront life’s challenges. This study proposes a technique employing Natural Language Processing (NLP) tools like Linguistic Inquiry and Word Count (LIWC), NRC-emotion-lexicon, and vaderSentiment to analyze social media posts, extracting psycholinguistic, emotional, and sentimental features from a hope speech dataset. The findings of this study reveal distinct cognitive, emotional, and communicative characteristics and psycholinguistic dimensions, emotions, and sentiments associated with different types of hope shared in social media. Furthermore, the study investigates the potential of leveraging this data to classify different types of hope using machine learning algorithms. Notably, models such as LightGBM and CatBoost demonstrate impressive performance, surpassing traditional methods and competing effectively with deep learning techniques. We employed hyperparameter tuning to optimize the models’ parameters and compared their performance using both default and tuned settings. The results highlight the enhanced efficiency achieved through hyperparameter tuning for these models.

An iteratively optimized downscaling method for city-scale air quality forecast emission inventory establishment

Air quality models (AQMs) are pivotal in forecasting air quality and shaping pollution control strategies. Nonetheless, the effectiveness of AQMs is often compromised in many cities due to the absence of accurate local emission inventories. To address this gap, this study presents a novel AQM-ready emission inventory generation technique with iterative optimization ability for city-scale applications in China. An efficient emission processing tool was introduced in this study, which utilizes the High-Resolution Multi-resolution Emission Inventory for China (HR-MEIC) as input. Using environmental observations and a region map, the tool can justify emissions of different regions iteratively. With the iterative optimization method, the model performance can be notably improved even without local emissions. The optimization was realized by splitting model-ready emissions into different regions and adjusting the emissions using scale factors calculated with the modeling results and the observations of each region. This methodology was applied to the Eight Cities in the Chengdu Plain (CP8C), located in the western margin of Sichuan Basin with complex topography and meteorological conditions, southwestern China, monthly throughout 2023. Air quality modeling was carried out using Weather Forecast and Research Model (WRF) and the Community Multiscale Air Quality Model (CMAQ). The results showed that the optimization acquired a good performance after five cycles for PM2.5 and NO2, with correlation coefficients (R values) surging from 0.62 and 0.37 to 0.77 and 0.73, respectively, while their normalized mean bias (NMB) substantially decreased from 22.8 % and 100.4 % to 3.6 % and 3.3 %. The underestimation on O3 concentration was also improved by the optimization, although enhancements in O3 modeling remained modest. This technique provides an easy-to-copy method to generate reasonable AQM-ready emission files with open emission data and observation data, which would be beneficial for the cities' air quality forecast in cities without local emission inventories.

A hybrid framework with large language models for rare disease phenotyping.

Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. SemEHR, a dictionary-based NLP tool, is employed to extract rare disease mentions from clinical notes. To refine the results and improve accuracy, we leverage various LLMs, including LLaMA3, Phi3-mini, and domain-specific models like OpenBioLLM and BioMistral. Different prompting strategies, such as zero-shot, few-shot, and knowledge-augmented generation, are explored to optimize the LLMs' performance. The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. LLaMA3 and Phi3-mini achieve the highest F1 scores in rare disease identification. Few-shot prompting with 1-3 examples yields the best results, while knowledge-augmented generation shows limited improvement. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients. The hybrid approach combining dictionary-based NLP tools with LLMs shows great promise for improving rare disease identification from unstructured clinical reports. By leveraging the strengths of both techniques, the method demonstrates superior performance and the potential to uncover hidden rare disease cases. Further research is needed to address limitations related to ontology mapping and overlapping case identification, and to integrate the approach into clinical practice for early diagnosis and improved patient outcomes.

Text Mining Strategies: RoBERTa Optimization for Efficient Pain Assessment in Hospice Care

The hospice unit in medical care offers comprehensive, personalized care to patients, yet the recent epidemic and associated illnesses have strained medical resources, leading to a shortage in capacity. The necessity for frequent physiological documentation and patient assessments places a considerable burden on the nursing staff, particularly in the context of limited personnel. This study addresses this challenge by leveraging natural language processing to aid in the evaluation of pain indices, aiming to enhance implementation quality and reduce associated costs. Three BERT models— BERT, MacBERT, and RoBERTa were employed for training purposes. Among these models, RoBERTa demonstrated exceptional performance, achieving an impressive accuracy rate of 99%. This research highlights the potential of natural language processing tools, specifically the RoBERTa model, in alleviating the workload of nursing staff and improving the efficiency of pain assessment in hospice care during times of heightened demand and limited resources.

Seismic imaging of deep‐seated gold deposit and host rocks through a reappraisal of legacy seismic data in the Fochville mining area, South Africa

AbstractReappraisal of legacy reflection seismic data has shown to deliver value in mineral exploration, particularly in brownfield settings. In this work, we demonstrate how the reappraisal and processing of legacy reflection seismic data can be advantageous in the mineral exploration industry. We use today's standard seismic processing tools to improve the imaging of deep and complex geological structures that host mineral deposits. The recovered and processed 25.3 km long legacy seismic profile in this study was acquired in 1983 by the Gold Division of Anglo‐American as part of the Witwatersrand Gold Fields exploration program. This study aims to improve the imaging of the Ventersdorp Contact Reef gold‐bearing horizon (termed reef), a world‐class gold deposit (2 m thick) situated at depths between ∼2400 and ∼4100 m below the ground surface near South Deep Gold Mine in Fochville, South Africa. The final processing results from the pre‐stack time and phase‐shift migration approaches clearly reveal a dipping reflection associated with the gold‐bearing horizon and major steeply dipping faults that crosscut and displace the deposit. The final results are integrated with borehole information, 1D synthetic modelling and aeromagnetic data to constrain the structural interpretation. In particular, 1D synthetic simulation and borehole data constrain the depth position of the gold deposit. The magnetic data provides additional constraints on the complex faulted blocks of the host rocks such as the intrusions that may have a direct impact on ore resources and evaluation. The mining companies, such as South Deep Gold Mine, operating closer to the seismic profiles can use this new structural information to update the current geological models and improve future mine planning and designs, thus providing some insight into the prospectivity of unmined ground.

Deep learning on chest X-ray and computed tomography scans for detection of COVID-19 as a part of a network-centric digital health stack for future pandemics

Developing a reliable rapid screening protocol for highly infectious diseases like COVID-19 is of paramount interest since it facilitates the isolation of infected patients from the rest of the population. Reverse-transcription polymerase chain reaction (RT-PCR) test is presently the most widely accepted gold-standard test to detect COVID-19. In this method, the RNA of the virus is duplicated by a process called reverse transcription to form DNA for facilitating the copying process. Fluorescent dye is attached to the viral genetic material and copied billions of times through the process called polymerase chain reaction. Enhanced fluorescence is used to identify the presence of genetic material of the virus. These tests are time-consuming and have significant false negatives, i.e., a person with COVID-19 might be categorized as not having the virus. Large-scale RT-PCR testing has its own share of problems such as logistics, availability and affordability in underdeveloped nations, and reliability of the test results. Machine learning algorithms can act as a cheaper supplementary/alternative diagnostic tool for the testing process. In the current study, using publicly available chest X-ray image datasets, different convolutional neural network (CNN)-based models were developed for efficient identification of COVID-19 infected patients, and their efficacies were compared. Key innovations in training the CNNs are discussed. Our results indicate that EfficientNet, SeResNext, and ResNet are best at classifying normal, pneumonia and COVID-19 cases, respectively. The ResNet architecture with transfer learning performed best at detecting COVID-19 with an accuracy of 94%, a rate far superior to that in the RT-PCR test, which is typically in the range of 70 &amp;ndash; 80%. This is particularly attractive as an additional noninvasive protocol since such technology-augmented detection is likely to help in reducing the psychological refractory period due to COVID-19 infections. Toward the healthy lung initiative in the post-COVID-19 era, we propose close coupling of the present diagnostic protocols with digital approaches to ensure more reliable personal care within the ambit of large-scale pandemic control mechanisms. Such integration with emerging technological tools can create a benchmark for the first line of defense against future global pandemics.

A hybrid approach for Bengali sentence validation

Bengali is the official language of Bangladesh and is widely used in Bangladesh and West Bengal in India. Due to the growing accessibility of the internet and smart devices, the use of digital text material and documents in Bengali is growing with time. An automated Bengali Sentence Validation System is proposed in this study to effectively determine the correctness of sentences in such extensively available Bengali content. As far as we know, no substantial work has been done in the field of Bengali Sentence Validation utilizing deep learning approaches. Due to the lack of linguistic resources, sophisticated Natural Language Processing tools, and benchmark datasets, developing an automated Sentence Validation System for a limited-resource language like Bengali is challenging. Additionally, Bengali Sentences come in two morphological varieties (Sadhu-bhasha and Cholito-bhasha), making the validation process more challenging. The proposed automated Bengali Sentence Validation system contains the CNN-BiLSTM hybrid classifier model. As of now, there is no standard dataset for Bengali sentence validation. Due to the lack of a standard dataset, we collected Bengali sentences from different sources in Bangladesh and developed a Bengali Sentence Validation (BSV) Dataset with around 5000 labelled sentences arranged into two categories such as correct and incorrect. Experimental results demonstrate that the proposed system outperformed other classifier models and existing approaches for Bengali Sentence Validation and is able to categorize a wide range of Bengali sentences based on their correctness. The system’s F1 score for the Bengali Sentence Validation is 98%.

Laser-assisted automated tape laying: Effects of placement rate and heated tooling on layer bonding and tensile properties

This paper investigates the processing parameters placement rate and tooling temperature of laser-assisted automated tape laying (ATL). Plates with carbon fiber-reinforced polyether ether ketone (CF/PEEK) were produced at 6 m/min, 9 m/min and 18 m/min, and 23°C and 250°C tooling temperature, respectively. Correlations of processing conditions with micrographs, void content, thickness, the degree of crystallinity and the shear strength of the compression shear test, including a fractographic analysis, were derived. Processing conditions that increase the degree of crystallinity were only found to improve the compression shear strength at 6 m/min placement rates, primarily due to a void content below 3.4 % and degree of crystallinity above 26 %. The fractographic analysis derived from the compression shear test revealed a strong correlation between the degree of crystallinity and the size of ductile drawings. In contrast, the transverse tensile test showed no influences on the ATL processing conditions, whereas the tensile strength in fiber direction decreased by 11 % from a placement rate of 6 m/min to 18 m/min. ATL with a 250°C tooling temperature increased the strengths by 14 % compared to those at room temperature. A further tensile strength improvement of 0° and 90° to 1754 MPa and 75 MPa was obtained by a subsequent out-of-autoclave consolidation using vacuum bagging.

Cutting speed and behaviors of ice using Yb-doped fiber laser

The use of a laser to cut or drill ice has been proposed and demonstrated multiple times in previous decades as a novel, but never adopted, machining tool in glaciology and paleoclimate studies. However, with the rapid development of high power fiber-laser technology over the past few decades, it is timely to perform further studies using this new tool. An investigation is made herein on the cutting of ice using a Yb-doped fiber laser emitting at a wavelength of 1070 nm, the most extensively developed and highest power fiber laser technology, in pulsed and continuous-wave operation. Visible-light observations of clear tap water ice samples, moving at a constant velocity relative to a pulsed laser beam, demonstrate a linear relationship between the duration of a millisecond-range laser pulse and the depth of the meltwater-free cut formed in response. Thermal imaging of the irradiated face shows that peripheral heating trends linearly for pulse lengths greater than 5 ms. A comparison of pulse trains with a constant time-averaged power suggests that shorter pulses are advantageous in slot-cutting efficiency and in minimizing visible alterations in the surrounding ice. These results demonstrate the viability of powerful fiber-compatible lasers as a tool for ice sample retrieval and processing.

Latent personality profiles of analog astronauts: An unsupervised clustering method analysis

The selection of astronauts for space missions is an important process that involves the assessment of multiple factors, including personality traits. Previous studies have corroborated that these traits exert a significant influence on the performance and adaptation of the crew during the execution of space missions. In this study, International Personality Item Pool - NEO – 120 (IPIP-120) was used to assess the personality of participants in a sample of 43 analog astronauts who took part in 9 different analog missions between 2022 and 2023. The objective was to identify specific personality profiles among the analog astronauts using the unsupervised k-means clustering method. The centroids of each cluster were analyzed to describe distinctive personality profiles. The results revealed the existence of two distinct personality clusters among the analog astronauts. The first cluster showed a greater propensity toward adventure, intellect, goal striving, extroversion, and cooperation. In contrast, the second cluster exhibited lower scores on these traits. The identification of these two differentiated personality profiles reinforces the importance of personality assessment as a valuable tool in the astronaut selection process. Furthermore, the application of the k-means method demonstrates its utility as an effective technique for identifying underlying patterns in complex datasets. These findings may inform future astronaut selection strategies, contributing to the formation of more effective and cohesive teams for space missions.

Public Participation Monitoring: Social Media Data Mining and Analysis of User Engagement Patterns

Social Media Platforms (SMP) like Twitter and Facebook have become public influencing medium in today’s digital age which facilitate community interactions. These necessitates the monitoring of public participation in these SMP. This work performs such an study by examining the user engagement patterns in social media in Indonesia during the national elections (#Pemilu2024), Ramadan celebrations (#Ramadan2024), and climate change discussions (#ClimateChange). The data for the study was collected during a period of six months from (January to June 2024) using event-specific hashtags and keywords. To identify the user engagement patterns datamining and Natural Language Processing (NLP) tools were utilized. The findings show that the Twitter platform has higher user engagement in morning with news updates and visual updates at evening. The Facebook show user engagement in afternoon with videos and evening engagement with shared articles. The Sentiment Analysis (SA) and network analysis was performed over the dataset and the findings have shown that higher positive sentiments towards elections and Ramadan but argumentative towards climate change discussions. The Twitter show rapid communication effectiveness compared to Facebook. Further the youth prefer faster update and older expect detailed content sharing.