Reviewerly/exHarmony huggingface dataset:
OPTIMISASI PEMILIHAN FITUR UNTUK PREDIKSI GAGAL JANTUNG: FUSION RANDOM FOREST DAN PARTICLE SWARM OPTIMIZATION

Heart failure is a serious, life-threatening cardiovascular disease that increases with age and unhealthy lifestyles. Early prediction is essential to provide timely treatment and reduce mortality. The use of machine learning techniques, especially the Random forest (RF) method, for predicting heart failure has been previously researched, so the problem that occurs is that the RF method does not have maximum results because of irrelevant features. Selection of relevant features is a key step in building an accurate prediction model. Particle Swarm Optimization (PSO) is used to improve feature selection by searching for optimal combinations. The aim of the research is to reduce the mortality rate by improving the RF method with relevant features so as to increase the accuracy of predictions with Fusion RF and PSO. The results show an increase in accuracy of 02.78% to 87.33% with PSO, although the AUC decreased by 0.031%. The advantage of PSO is a significant increase in accuracy, but the disadvantage is a slight decrease in AUC. Future developments could explore how to address AUC degradation without compromising accuracy and transmitting additional relevant features.
whiteheadi metafizika megjelenése három fallácia fényében

Whitehead and the method How can we interpret the emergence of Whitehead's metaphysical and cosmological project on the map of 20th-century&nbsp; philosophy after Kant's critical turn? How can this project be related to Whitehead's contemporary philosophical tendencies, such as the pragmatist movement that was developing in America? This paper assumes a fundamental link between the two questions, namely that it is necessary to clarify the methodological reflections that define their philosophies in interpreting Whitehead and the pragmatists, including William James. In Whitehead’s case, this also means that the goal of metaphysics and cosmology can only be understood as a systematic elaboration of the method. And for Whitehead, this constructing process (of the method) is the main task of speculative philosophy.
Abstract WP178: Evaluation of Low NIHSS Subitems Association With LVO Detection in Large Telestroke Database

Background: The benefits of mechanical thrombectomy in low NIHSS score (&lt;6) is unclear and undergoing investigation. Current acute stroke guidelines do not address the utilization of computed tomography angiography (CTA) in low NIHSS largely due to a paucity of data leading to great variation in clinical practice. Understanding the predictive value of NIHSS subitems for LVO may help guide imaging selection. Methods: Acute stroke consultations seen in the emergency department in 227 facilities (27 states) from July 1, 2021 to December 31, 2021 were extracted from the TeleCare by TeleSpecialists TM database. The encounters with CTA performed were reviewed for LVO detected, and NIHSS score with individual subitems. Logistic regression analysis was performed on the NIHSS subitems in total and at NIHSS cutoff points of ≤6, ≤4, and ≤2. Results: A total of 12,668 patients had complete NIHSS subitems available with 1,459 (11.5%) having an LVO and 11,209 without an LVO were extracted. There is a significant difference in the ORs for each subitem. The ORs for patients with NIHSS ≤6 subitems: Level of Consciousness 1.11 (p=0.626), Consciousness Questions 1.36 (p&lt;0.001), Consciousness Commands 1.94 (p&lt;0.001), Best Gaze 1.61 (p=0.061), Visual Field 1.65 (p&lt;0.001), Facial palsy 1.71 (p &lt;0.001), Motor Arm-left 1.43 (p &lt;0.001), Motor arm-right 1.03 (p=0.840), Motor leg-left 0.97 (p=0.758), Motor leg-right 0.83 (p=0.103), Limb ataxia 1.09 (p=0.545), Sensory 0.67 (p=0.001), Language 1.92 (p&lt;0.001), Dysarthria 1.81 (p&lt;0.001), and Neglect 5.78 (p&lt;0.001). Trends in subitems with each lower cutoff were increased odds of LVO with Consciousness Commands, Visual Fields, Language, and Neglect, and lower odds of LVO with Sensation. Conclusions: This analysis shows the limitations of the NIHSS subitems to predict LVO. Despite the trend of lower odds of LVO for Sensation, even with the NIHSS cutoff of ≤2 there would have been 7 LVOs missed. There are 6 subitems showing statistical significance at an NIHSS cutoff of ≤2 which suggests that an NIHSS cutoff, even with these identified subpoints, is not a good CTA candidate selection tool for the detection of all LVOs.
Interaction of Digital Trends and Sustainable Development: The role of Contemporary Art

The purpose of the work is to analyze the impact of digital technologies on the sustainable development of contemporary art - an overview of the main trends that have emerged as a result of the integration of new media into artistic practices. The article methodologically classifies various genres of digital art by means of a critical qualitative analysis of practices, artworks, installations, exhibitions, collectives and events at the forefront of artistic innovation with new technologies. The scientific novelty lies in a comprehensive analysis of digital art through the prism of the concept of sustainable development. The work considers digital art not just as another technological innovation, but as a full-fledged artistic phenomenon of our time. The systematization of artistic practices associated with the use of the latest technologies is carried out, their common conceptual foundations are revealed. In addition, the article deals with issues of environmental sustainability, social responsibility, inclusiveness of digital practices. The dilemmas around the balance of technological innovation and artistic freedom from the perspective of sustainability ethics are outlined. The conclusions provide a conceptual understanding of optimal pathways for developing digital art in relation to societal sustainability.&#x0D; Keywords: digital art, sustainable development, new media art, network art, generative art, interactive art, AI, VR, AR, digital culture
Sentiment Analysis Using Convolutional Neural Network (CNN) and Particle Swarm Optimization on Twitter

Over time, social media has always changed quickly. People can voice their ideas on various topics and communicate with each other through social media. One social media platform that allows users to express their ideas through tweets is Twitter. Sentiment is the route via which each person can express their ideas on a variety of subjects. The sentiment can be positive or negative. Sentiment analysis can be used to determine how Twitter users feel about particular subjects. Sentiment analysis on popular subjects in 2023, specifically the 2024 presidential contenders, will be done in this research. The dataset used in this research consists of 37,391 entries with 5 keywords. The research aims to understand how Twitter users respond and express their sentiments towards the presidential candidate through the use of deep learning classification techniques with Convolutional Neural Network (CNN), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) method, and feature expansion with Word2Vec. Furthermore, this study employs Particle Swarm Optimization as an optimization technique to enhance the sentiment analysis model's performance. The test's results demonstrate a high degree of accuracy, offering a comprehensive picture of Twitter users' sentiments and perspectives toward the 2024 presidential contenders. This research helps to understand the dynamics of public opinion in the political context. Based on the evaluation results of the research, it yielded an accuracy of 78.2%, showcasing an improvement of 10.07% compared to the baseline.
Analisis Instrumen Model Mitigasi Banjir di Indonesia

One of measures to reduce the flood impact in Indonesia has been done by flood emergency response action plan. The action plan is generated from decision making process assisted by instrument such computer models which have capability in producing scenarios and decision to be taken. This research is conducted to identify how the written procedures (delivered by international and national agencies) related to flood emergency response, being accommodated by those (computer) models. By using qualitative analysis, the element procedures of an effective flood emergency response model have been reviewed. The result shows that the detailed information such human and material resources, facilities information, and warning time-lag, have not been properly and entirely accommodated by the (computer) model. Highlights are also given to propose more effective instrument to improve the management of flood crisis.
Perbedaan Nilai Indeks Eritrosit Pada Sampel Darah Vena Dengan Pengambilan Posisi Duduk Dan Posisi Berbaring

The erythrocyte index or Mean Cospuscular Value is an average value that can provide information about the average erythrocyte and the amount of hemoglobin per-erythrocyte. The erythrocyte index consists of Mean Corpuscular Volume (MCV) or the average volume of an erythrocyte, Mean Corpusculara Hemoglobin (MCH) or the amount of hemoglobin per-erythrocyte, Mean Corpuscular Hemoglobin Concentration (MCHC) or the concentration of hemoglobin obtained per-erythrocyte. Erythrocyte index examination is more specifically used to determine what type of anemia is suffered by someone who experiences it. This study aims to determine the difference in the results of the examination of erythrocyte index values in venous blood sampling by taking sitting and lying positions. This research method is Crossectional, research conducted at Bunda Hospital Palembang. The population taken was female students of the S.Tr study program. Medical Laboratory Technology Muhammadiyah Palembang, Samples used in this study amounted to 31 samples using Purposive Sampling. The study obtained the results of MCV sitting position 84.88 and lying position 84.34 fl, MCH obtained an average value in sitting position 28.09 and lying position 28.05 pg / cell and MCHC examination obtained an average value in sitting position 33.05 and lying position 33.22 g / dl which means there are differences in erythrocyte index examination in venous blood sampling with sitting position and lying position. It can be concluded that venous blood sampling in the sitting position is not recommended for erythrocyte index examination, because the examination results are different in the sitting position.
Teachers’ Strategies and Challenges to Promote Students’ Active English Learning in SMK Muhammadiyah 1 Palu

Student engagement has an important aspect in achieving excellent learning outcomes for learners and further improving their abilities. So teachers must have strategies to encourage student involvement. The aim of this research was to investigate the strategies the teachers use in activating students active learning in teaching and learning process and the challenges the teachers face in promoting students active learning. The researcher used qualitative research. The place of the research was in SMKS Muhammdiyah 1 Palu that located in Letjend Seoprapto. The Subject of the research were all English teachers at SMK Muhammadiyah 1 Palu that consist of four teachers. The data collection techniques that wil be used are: In depth Interview, Observation, and Documentation. The data will be analyzed in 3 steps, are Data reduction, data display, and conclusion. After conducting a research in SMK Muhammadiyah 1 Palu, it is found that in stimulating students’ active English learning, the teachers use six strategies; Questionning &amp; discussion, visual based instruction, rolep lay, game, project based learning, and discovery based learning.From those six strategies the teachers used in the previous explanation, there are four strategies that are used dominantly; questioning &amp; discussion, visual based instruction, project based learning, and discovery based learning.researcher proposes that future researchers interested in the same issue might do in-depth research on teacher ways to encourage students' active learning in English. For instance, the specific strategy that can stimulate the specific skill.
Evaluating the Performance of Mixed Zero-Inflated Poisson Regression Models with Time-dependent and Time-independent Covariates

One of the issues that researchers may encounter in count data is having many zeros. One of the solutions to model these data is using zero-inflated Poisson (ZIP) regression models. Recently, researchers have started to model longitudinal count data with time-dependent covariates. However, it has not been considered whether a model with time-dependent covariates provides a better fit than a model with time-independent covariates. In this paper, the fit between a mixed ZIP model with time-dependent covariates and a mixed ZIP model with time-independent covariates is compared using simulation. Using the deviance information criterion as a measure of fit, we found that the model with time-dependent covariates exhibits a better fit than the model with time-independent covariates. KEYWORDS correlated data, count data, excess zeros, longitudinal, mixed models, model fit
THE EFFECT OF IMMERSIVE TECHNOLOGY ON ENHANCING STUDENT LEARNING:

Education is one of the fundamental factors influencing national development. Educators are required to constantly explore the newest and creative strategies to deliver topics to students and promote students’ engagement in the learning process. One technology that has numerous potentials to facilitate the education process is immersive technology. Immersive technology has the potential to improve student achievement by enabling better learning outcomes. This study aims (1) to analyze the trends and impact of immersive technology usage in education; (2) to identify immersive technology that supports students’ learning processes in the pandemic era; and (3) to identify the usage of learning theories in educational application development that implement immersive technology to enhance students’ learning outcomes. This study examined seventeen selected studies after adopting the selection process of the systematic literature review process by Kitchenham. The selected studies were published between 2018 and 2022 by Emerald Insight, IEEE Xplore, and Scopus. There are three kinds of immersive technology identified in this study: virtual reality, augmented reality, and mixed reality. The study showed the nine types of students’ learning outcomes, and the results showed that using immersive technology significantly improved student learning compared to traditional methods. Furthermore, thirteen learning theories were adopted as the basis for developing educational applications. Future research directions are also suggested to continue developing promising and limited new technologies to enhance the variety of education-oriented applications. Immersive technologies designed for learning are still on a long journey, including considerations of readiness and potential effects that may arise for users such as privacy and security concern.
Abstract WP37: Way Beyond the Call: An Extended Collaborative Feedback Project With EMS

Background/Purpose: Emergency Medical Services (EMS) providers assess, treat and transport patients to destinations that provide the most appropriate emergency care. EMS provides the first crucial link in the chain of survival for patients suffering from life-threatening conditions. Stroke is a time-sensitive emergent condition which requires urgent evaluation, treatment, and expeditious transport to stroke-specialty centers. We noticed that EMS was not providing us with important information that could impact our initial stroke care. Our 21-month collaborative feedback project was created to provide EMS personnel who frequent our Emergency Department (ED) the tools necessary to administer high-quality, expeditious care. Our goal was to improve information provided to the stroke team and ED upon patient arrival by improving and sustaining three metrics by 20% within a 21-month period, by providing feedback to EMS, which include last known well time (LKWT), caregiver contact information (CCI), and mLAPSS documented (mLAPSS). Methods/Implementation: We collaborated with the Beverly Hills Fire Department (BHFD) educators from July of 2021 through March of 2023, providing a continual feedback loop for all EMS runs with an adjudication of stroke and TIA. Our feedback tool included quality metrics with benchmarks determined by the Los Angeles County EMS Agency, in conjunction with the BHFD educators and our team. We performed chart reviews and extracted data pertinent to each run. We summarized the hospital course and disposition for each patient and the feedback tool was distributed to the EMS providers for review. Data was collected and analyzed over seven quarters (Q3 and Q4 of 2021, Q1-Q4 of 2022, and Q1 of 2023, respectively). Results/Conclusions: Sustained improvements were noted in documentation. We achieved 20% improvement in LKWT (from 78% in Q3-Q4 2021, 98% in Q1-4 2022, 98% in Q1 2023), CCI collected (from 29% in Q3-Q4 2021, 75% in Q1-4 2022, 100% in Q1 2023), and mLAPSS completion was 8% improvement (from 89% in Q3-Q4 2021, 97% in Q1-4 2022, 100% in Q1 2023). An unanticipated result of this project was a halo effect of improved destination decisions from EMS. We will continue to track and trend these metrics and work closely with our colleagues in EMS.
Unravelling the tectonic phases: The impact of the South Caspian Block on Late Cenozoic deformation in the Central Alborz, Iran

Paleostress reconstruction through polyphase fault-slip data of a multi-deformed region suffered collisional tectonics can lead to detecting the stress phases. Based on earthquakes’ focal mechanisms and morphotectonic features, multiple deformation phases model has been proposed for the Alborz Mountains located in the collision zone between the Arabian (Central Iran) and Eurasian (South Caspian block) plates during the Late Cenozoic. In this study, paleostress analysis has been carried out in an area bounded by two (Kandovan and Taleghan) regional faults in the Central Alborz Mountains using fault-slip data. This analysis resulted in the identification of three main tectonic phases. The first, compressional phase is proposed to cause the inversion of the Alborz Mountains’ major initial normal faults to reverse faults during the convergence of the Arabian and Eurasian plates. The second, transpressional phase is offered as a factor for the reactivation of the hidden Alborz basement faults to form the NE-striking left-lateral strike-slip faults on the sedimentary cover. The third, transtensional phase is suggested to be responsible for the development of the NNE–SSW left-lateral transtensional faults. It is proposed that the second and third paleostress phases are affected mainly by the indentation of the South Caspian Block into the Alborz Mountains during the Late Cenozoic.
Implementation of Association Rules Algorithm to Identify Popular Topping Combinations in Orders

Association rule is a data mining technique to find associative rules between a combination of items. This research aims to apply association rules algorithm in identifying popular topping combinations in food orders. This application aims to help restaurant owners or food businesses understand their customers' preferences and optimize their menu offerings. Data obtained from kaggle, the association rules algorithm is applied to this dataset to identify patterns or combinations of toppings that often appear together in orders. The results of this study show toppings with chocolate as a popular item in orders. These findings can provide valuable insights for food business owners in structuring their menus and determining attractive offers for customers. This study also applied a comparison between the apriori, fp- growth and eclat algorithms, with the result that the best item transaction rule was found: a combination of dill &amp; unicorn toppings with chocolate with 60% confidence. Overall, the application of eclat algorithm in this study provides the best performance with higher execution speed, thus providing insight into customer preferences regarding topping combinations in food orders. Despite the shortcomings of the data form from this study, it is expected to help business owners in optimizing their offerings, increasing customer satisfaction, and improving their business performance.
Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

The hallmarks of Obsessive-Compulsive Disorder (OCD) are intrusive, anxiety-inducing thoughts (called obsessions) and associated repeated activities (called compulsions). To understand the patterns and relationships between OCD data that have been obtained, data will be grouped (clustering). In clustering using several clustering algorithms, namely K-Means, BIRCH, In this work, hierarchical clustering was used to identify the optimal cluster value comparison, and the Davies Bouldin Index (DBI) was used to confirm the results. Then the results of the best cluster value in processing OCD data are using the BIRCH algorithm in the K10 experiment which gets a value of 1.3. While the K-Means algorithm obtained the best cluster at K10 with a value obtained of 1.36 and the Hierarchical clustering algorithm also at the K10 value of 2.03. Thus in this study, the comparison results of the application of 3 clustering algorithms obtained results, namely the BIRCH algorithm shows the value of the resulting cluster is the best in clustering OCD data. This means that the BIRCH algorithm can be used to cluster OCD data more accurately and efficiently.