bluuebunny/crossref_metadata_2025 huggingface dataset:
MANEJO DA ENTEROCOLITE NECROSANTE NO NEONATO PREMATURO
Authors: ALENCAR JEICEANE PELAES , VIEIRA CARLOS VITOR MIRANDA , LOPES GEOVANNI EDUARDO SOUZA , GUIMARÃES PAULYNE SOUZA SILVA , OLIVEIRA DENISE MARY COSTA DE , SOUSA ALINE ANDRADE DE , SATO DIANA ALBUQUERQUE , JACOMEL RODRIGO JANUÁRIO , JACOMEL BRUNA GRAZIELLE CARVALHO
Published: November 2024

Objetivo: Esta pesquisa objetivou realizar um levantamento bibliográfico através de revisão
integrativa sobre o manejo da enterocolite necrosante no neonato prematuro. Metodologia: O estudo
é uma revisão integrativa de literatura baseada na análise de artigos publicados em revistas científicas
com o intuito de explorar o manejo da enterocolite necrosante no prematuro. As bases de dados
eletrônicas PubMed, SciELO, LILACS, Cochrane Library, Web Of Science e ScienceDirect foram
consultadas retrospectivamente nos últimos 5 anos - 2019-2023 (período escolhido arbitrariamente).
Foram utilizados os seguintes Descritores em Ciências da Saúde: “enterocolite necrosante” AND
“prematuridade”; sem restrição de idiomas. Resultados e Discussão: Foram incluídos neste estudo,
85 artigos. Dentre os achados, o aleitamento materno na prevenção da ECN e sua reintrodução de
forma precoce no tratamento da doença destacam-se, assim como o uso de probióticos de forma
preventiva. A laparotomia permanece sendo o principal tratamento cirúrgico nos casos de ECN grave,
e não há um esquema de antibióticos preconizado para o tratamento. Considerações Finais: A ECN
é um desafio para a neonatologia e ainda não há um manejo ideal que seja padrão para os acometidos
pela doença, o que aumenta a morbimortalidade.
DROGAS ILÍCITAS NA GESTAÇÃO: CONSEQUÊNCIAS PARA GESTANTE E O RECÉM-NASCIDO
Authors: ASSUNÇÃO ELIZAMARA DA SILVA , GUSTAVO MILENA BOMFIM , CAVALCANTE NATHALIA ALMEIDA TENÓRIO , DANTAS IGOR GABRIEL RIBEIRO , CARNEIRO MARIA LAIANE DE SOUZA , CERQUEIRA JADIANE BARROS DE , SILVA JOSY RAFAELI BERNARDINO DA , CERQUEIRA MARIA CLARA NASCIMENTO , VIRGOLINO EDUARDA GABRIELLA PEREIRA , GOMES WILIANNE DA SILVA
Published: November 2024

Introdução: O uso de substâncias ilícitas durante a gestação é um problema de saúde pública, que
afeta tanto a gestante quanto o feto. Mediante a isso, esta pesquisa tem como objetivo evidenciar
alterações que podem ser desencadeadas, através do usos destas drogas, no neonato e na gestante.
Metodologia: Trata-se de uma revisão narrativa da literatura realizada durante o mês de Julho de
2024 por meio da base de dados do Google Acadêmico. Após, análise dos artigos encontrados e
filtrados com os critérios de inclusão, 15 artigos foram selecionados para compor o estudo.
Resultados e Discussão: As evidências literárias demonstram que o consumo de drogas como
cocaína, crack e outras substâncias psicoativas está diretamente ligado a complicações severas,
incluindo distúrbios no desenvolvimento do feto, prematuridade, baixo peso ao nascer, malformações
congênitas e até a morte neonatal. Além disso, o uso dessas substâncias pode contribuir para
problemas de saúde mental e física nas mães, como hipertensão, descolamento prematuro de placenta
e transtornos psiquiátricos, agravando ainda mais os riscos durante o período gestacional.
Considerações Finais: Diante dessa complexidade, torna-se evidente a necessidade de uma
abordagem multidisciplinar para enfrentar o problema. Isso inclui a implementação de políticas
públicas robustas que foquem na educação preventiva, especialmente desde a adolescência, na
ampliação do acesso a cuidados de saúde, e no oferecimento de apoio psicológico e social às gestantes
em risco.
EXPERIÊNCIAS DAS MÃES DIANTE DO NASCIMENTO DO RECÉM-NASCIDO PREMATURO DURANTE COVID-19
Authors: LINHARES MARIA DAS GRAÇAS CRUZ , NOGUEIRA DENISE LIMA , PONTES RENATA SOARES , ROMÃO FRANCISCA SAMILA PINTO , MACHADO MARCIA MARIA TAVARES
Published: November 2024

Objetivo: compreender a experiência materna diante do nascimento prematuro na pandemia,
destacando desafios e estratégias de apoio. Metodologia: trata-se de pesquisa qualitativa, realizada
em Sobral, Ceará, com mulheres que tiveram filhos prematuros entre junho e dezembro de 2020. O
número de participantes foi determinado pela saturação das informações e para coleta de dados
realizou-se entrevistas semiestruturadas. As informações foram analisadas pela Análise de Conteúdo
Temática de Bardin. Resultados e Discussão: A separação inicial mãe-bebê, devido à necessidade de
cuidados intensivos, e o medo de contaminação por COVID-19 foram os principais desafios
enfrentados. A pandemia intensificou a insegurança materna e reduziu o apoio social, impactando
negativamente a saúde mental das mães. Outro aspecto identificado na pesquisa foi a insegurança das
mães em lidar com a fragilidade dos bebês prematuros, pois se sentiam incapazes de realizar tarefas
simples. Considerações finais: As experiências das mães de bebês prematuros durante a pandemia
de COVID-19 evidenciam a necessidade de um suporte robusto e abrangente para enfrentar os
desafios impostos por crises sanitárias. O reconhecimento e a compreensão das emoções vivenciadas
por essas mulheres são fundamentais para o desenvolvimento de estratégias eficazes que promovam
a saúde mental e o bem-estar das mães e de seus bebês.
USO DE BIOMODELOS NO TRATAMENTO DE FRATURAS DA MANDIBULA ARTRÓFICA
Authors: Sena Leandro Rodrigues de , Ferreira Antônio Fabrício Alves , Martins Luiz Filipe Barbosa , Picanço Paulo Roberto Barroso , Becheleni Marco Túllio , Brito Edla Helena Salles de , Pereira Katia Caetana , Picanço Victor Vasconcelos , Leitão Francisca Luciana Adeodato , Melo Watuzi Barbosa de , Lima Glayce Maria Serra , Lessa Aliny Rodrigues , Cabral Kevin de Carvalho , Picanço Arthur Vasconcelos , Vasconcelos Miguel Barros de
Published: November 2024

O trauma facial é uma ocorrência comum no atendimento em pronto-socorro em todo o mundo. De acordo com a maioria dos dados epidemiológicos pesquisas, a mandíbula e os ossos nasais são os mais afetados. Os pacientes podem relatar dor, limitação funcional, disfagia, alterações oclusais e deformidades faciais, razão pela qual, na maioria dos casos, o tratamento cirúrgico é necessário Fraturas mandibulares não tratadas podem resultar em má oclusão, infecção e dor persistente; portanto, devem ser abordados precocemente para evitar complicações, consolidação viciosa e pseudoartrose de fragmentos ósseos. Tendo como principal estudo em restabelecer a oclusão e o contorno mandibular através da redução precisa dos segmentos ósseos. No entanto, a redução ideal pode ser difícil devido aos padrões complexos de fratura, ação muscular que favorece o deslocamento e tempo decorrido entre o trauma e o procedimento cirúrgico. Atualmente, a fixação interna estável é a técnica de escolha para fraturas simples, enquanto a fixação interna rígida é indicada em casos de fraturas cominutivas ou quando defeitos segmentares estão presentes. A fixação rígida busca restabelecer o contorno mandibular original e serve como armação para reconstrução óssea.

Palavras-chave: Fratura; Face; osso.
O USO DA LASERTERAPIA NA REGENERAÇÃO ÓSSEA EM IMPLANTODONTIA: UMA REVISÃO DE LITERATURA
Authors: Sena Leandro Rodrigues de , Ferreira Antônio Fabrício Alves , Martins Luiz Filipe Barbosa , Picanço Paulo Roberto Barroso , Becheleni Marco Túllio , Brito Edla Helena Salles de , Pereira Katia Caetana , Picanço Victor Vasconcelos , Leitão Francisca Luciana Adeodato , Melo Watuzi Barbosa de , Lima Glayce Maria Serra , Lessa Aliny Rodrigues , Cabral Kevin de Carvalho , Picanço Arthur Vasconcelos , Vasconcelos Miguel Barros de
Published: November 2024

A implantodontia revolucionou a odontologia e os conceitos de reabilitação oral estética e funcional pela proposta de osseointegração de implantes dentários, tornando- -se uma excelente alternativa na reabilitação de pacientes com perda dental parcial ou total. No entanto, o processo de osseointegração do implante dentário demanda tempo para que a reparação tecidual seja concluída. Nesse sentido, ao longo dos anos surge a busca por prognósticos clínicos e cirúrgicos mais previsíveis e favoráveis com o propósito de acelerar a osseointegração e proporcionar um pós-operatório mais confortável ao paciente. Diante desse contexto, o laser de baixa potência surge como uma alternativa eficaz acelerando o processo de cicatrização, efeitos analgésicos e antiflamatórios. A proposição do presente trabalho foi realizar uma revisão de literatura para avaliar a biomodulacão proporcionada com a aplicação do laser de baixa potência no pós-operatório e suas contribuições na otimização do processo de osseointegração de implantes diminuindo o tempo de recuperação do paciente. A busca bibliográfica foi realizada nas bases de dados SciELO, Lilacs e PubMed, por meio da combinação das seguintes palavras chaves consideradas descritores no DeCS (Descritores em ciências de Saúde): laser terapia, osseointegração, cicatrização, osso, terapia com luz de baixa intensidade, implante dentário endósseo e selecionando-se os artigos datados entre 2010 e 2022. Quatorze artigos foram selecionados de acordo com os critérios de inclusão e exclusão e lidos na íntegra. Concluiu-se, que o uso do laser de baixa potência no pós-operatório otimiza o processo de osseointegração de implantes dentários.

Palavras-chave: Osseointegração; Laser de Baixa Potência; Implantes Dentários.
The effect of (a)symmetry on the optimal toxin allocation
Authors:   Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA, Paranganat Vishnu , Rychta Jan ,   Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284, USA;, Taylor Dewey ,   Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA 23284, USA
Published: November 2024

When mathematical models of biological phenomena deal with an unknown parameter, it is often assumed that such a parameter follows a normal distribution. This introduces a symmetry assumption into the model. The purpose of this paper is to investigate and quantify the effect of asymmetry on model prediction. We introduce an asymmetry into a model of sexual conflict and toxin allocation by replacing a normal distribution by a shifted beta distribution. This way, we can naturally consider a large family of continuously changing distributions. We isolate the effect of skewness on the model prediction and demonstrate that in most cases, increasing skewness causes a slight increase in optimal toxicity allocation. We conclude that overall, the effect of the skewness is much smaller than the effect of the mean. In fact, for the particular model we studied, skewness does not seem to affect qualitative predictions.
Decarbonizing public transport systems in the UK: Initiatives and technologies for reducing carbon emissions
Authors: Uthman Opeyemi Abdullahi  , Adnan Adnan  , Abdullahi Ayobami Rafiu
Published: November 2024

The decarbonization of public transport systems in the UK is a critical step toward achieving the nation’s net-zero emissions targets by 2050. This article examines the various initiatives and technologies that are being implemented to reduce carbon emissions from buses, trains, and trams. Focusing on key solutions such as electrification, hydrogen fuel cells, and hybrid systems, it evaluates their potential through a cost-benefit analysis and assesses their impact on environmental sustainability. The paper also explores the UK government’s policy frameworks, including the Transport Decarbonisation Plan, which supports the transition toward cleaner, more efficient public transport networks. Through case studies from cities such as London, Glasgow, and Birmingham, the article highlights successful decarbonization strategies, emphasizing the importance of government, industry, and community collaboration. These efforts aim to create a sustainable public transport system that not only meets carbon reduction targets but also enhances urban air quality and public health.
Strategic integration of fintech in smart grids using fuzzy logic: Enhancing operational efficiency and sustainable energy management
Authors: Veera Vakkapatla
Published: November 2024

This research paper explores the strategic integration of Fintech innovations within smart grid systems, utilizing fuzzy logic to enhance operational efficiency and promote sustainable energy management. The study begins by identifying the key challenges faced by modern power systems, particularly in the context of integrating Fintech solutions with smart grid technologies. A comprehensive system model is developed, incorporating fuzzy logic algorithms to optimize decision-making processes related to energy distribution, financial transactions, and resource allocation within smart grids. The methodology involves simulating the proposed system using MATLAB, where fuzzy logic is applied to model and analyze the dynamic interactions between financial mechanisms and energy flows. The simulation results are meticulously analyzed to assess the performance improvements in terms of operational efficiency, reliability, and scalability. A comparative analysis with traditional models is also conducted, demonstrating the superiority of the fuzzy logic-based approach in managing uncertainties and enhancing system resilience. The findings of this study provide significant insights into the practical implications of Fintech integration in smart grids, offering novel perspectives for energy market participants, power system operators, and policymakers. The research concludes with recommendations for further exploration of fuzzy logic applications in smart grid systems and the potential for broader adoption of Fintech solutions in the energy sector.
Impact of anticoccidial treatment with Solo papaya seed powder on haematological parameters in Sasso broilers
Authors: Komlan Mawouli Wampah  , Kosi Mawuéna Novidzro  , Ale Gonh-Goh Ayéfouni  , Melila Mamatchi  , Kulo Abalo Essosinam
Published: November 2024

Coccidiosis is a highly contagious and damaging poultry disease. Despite its effectiveness against coccidiosis, chemotherapy has not been able to overcome certain difficulties, in particular the phenomenon of bioresistance acquired by the parasites, the rising price of anticoccidials, and the contamination of poultry products by toxic residues that are dangerous for consumers. As an alternative solution, Carica papaya is emerging as an interesting and less costly natural substance for eradicating coccidiosis. Our study aimed to evaluate the anticoccidial efficacy of the seed of Solo papaya and its effects on haematological parameters in Sasso broilers. Six-hundred-day-old chicks of Sasso breed, broiler type, were divided into four batches with five replicates per batch: untreated batch (T-), batch treated with amprolium 20% (T+), and two batches treated with papaya seed powder (5%), incorporated into the feed for one and two consecutive days per month, noted P1 and P2 respectively. Determination of number of eggs per gram (EPG) was based on Mc Master technique. Haematological parameters were determined from blood collected in tubes containing EDTA using Mindray BC-3000/China automated haematological analyser. Papaya seed powder Treatment showed anticoccidial efficacy, expressed by a significant drop in EPG in the treated batches. In addition, haematological parameters improved, notably an increase in haematocrit and a decrease in the rate of various leucocytes. Anticoccidial treatment with papaya seed powder (5%) resulted in an appreciable reduction in parasite load and an improvement in the haematological profile. The result application could serve as a sustainable alternative to synthetic chemical anticoccidial drugs.
Optimizing Attendance System: Integrating Liveness Detection and Deep Learning for Reliable Face Recognition
Authors: Santoso Joseph Teguh , Sediyono Eko , Hartomo Kristoko Dwi , Sembiring Irwan
Published: November 2024

The study focuses on using vitality detection and deep learning technologies in the context of facial recognition in an IT presence management project. The combination of deep learning with vitality detection provides a considerable advancement in security and effectiveness. This work integrated vitality-detecting technology with in-depth learning in facial recognition systems. Vitality detection technologies are used to verify the authenticity of persons by examining live indicators such as movements or facial expressions before face recognition. Meanwhile, deep learning is used to analyze and process facial photos correctly by learning from large amounts of data and recognizing facial features in depth. The study data set consists of 1300 photographs of professional school instructors taken with official authority. Model testing and training are carried out in the Google Colab environment, using Python and the Hardy package. The test findings showed an 87% accuracy in face recognition, proving the system's capacity to consistently identify persons and distinguish real from false ones. Furthermore, the performance of Liveness Detection achieves 92% accuracy, as does the integration of Live Detection technology with Deep Learning at 78%.
Improved SVM Classification Using Particle Swarm Optimization for Student Completion Prediction System
Authors: Asana I Made Dwi Putra , Oka I Dewa Gede Ari , Widyantara I Made Oka , Sandhiyasa I Made Subrata
Published: November 2024

Timely completion of a study program is crucial for evaluating the quality of universities. To achieve timely completion, student’s progress needs to be monitored early in order to ensure that they can complete the given task on time. This process is particularly important because universities often enroll thousands of students, thereby making individual supervision impractical. An effective solution to this problem is leveraging machine learning to develop a system that predicts whether student will complete the study without delay. Therefore, this study used Support Vector Machine (SVM) method for classification, with RBF kernel. Optimization of SVM classification was achieved by ensuring the values for Soft Margin C parameter and kernel parameter were correct. In addition, Particle Swarm Optimization (PSO) method was used to determine the optimal SVM parameter values. Consequently, the resulting model was evaluated using Cross Fold Validation. The optimized SVM parameter identified through PSO were gamma of 0.0085 and C of 0.4196. The average training accuracy recorded is 82.58%, with 81.22% validation, these results can be categorized into Good Classification. Finally, the application of PSO in optimization resulted in SVM models that avoided overfitting, as shown by the closeness of training and validation values.
MAC Address Classification in Privacy Issue Using Gaussian Naïve Bayes
Authors: Riadi Imam , Fadlil Abdul , Prabowo Basit Adhi
Published: November 2024

There have been several initiatives within standards committees to overcome privacy issues, including user tracking activity based on Media Access Control (MAC) addresses. The implementation of randomized MAC addresses on captive portals, with user-specific connection limits to address privacy concerns, introduces some problems. To address this issue, device removal based on OUI classification was proposed. Connection data taken from the RADIUS server were divided into two distinct classes, either random or not. Gaussian Naïve Bayes was utilized to classify the data with 16 distinct thresholds, and the solution with the highest accuracy was selected. The research produced results showing that all classifications had an accuracy above 96%. Values of 6 and 50% for Mac address thresholds and random percentage thresholds gave the highest accuracy of 98.1139%. This indicates that random Mac address classification in the real world can be done using the result.
LSTM Algorithm in Predicting Chronic Kidney Disease Optimized Using Genetic Algorithm
Authors: Achmad Brillyando Magathan , Sa'adah Siti , Kurniawan Isman
Published: November 2024

Chronic Kidney Disease is a health condition in which the kidneys experience a progressive decline in function. Kidneys are vital organs that filter waste and excess blood fluids. CKD can lead to excess products in the body and cause various health issues, so early detection of CKD is necessary. While traditional machine learning techniques have performed well in predicting CKD in existing studies, this study investigates the potential of long short-term memory (LSTM) optimized with Genetic Algorithm to enhance predictive accuracy and efficiency by optimizing its hyperparameters, including number of units, hidden layers, activation function, recurrent activation, and dropout rate. The result demonstrates that the optimized LSTM slightly performs better than without optimization, achieving higher precision, recall, accuracy, and f1 score by 100% respectively. This outstanding result can be attributed to several key factors, such as ensuring rigorous data preprocessing and utilizing k-fold cross-validation to make the model more reliable. This indicates the hybrid approach can be a powerful method for the early detection of CKD, leading to better patient outcomes. Despite the promising performance, further research is suggested, specifically using a larger dataset to ensure applicability to more general population and exploring other optimization methods to reduce computational cost.
Technical Analysis of the Indonesian Stock Market with Gated Recurrent Unit and Temporal Convolutional Network
Authors: Aisyah Siti , Angraini Yenni , Sadik Kusman , Sartono Bagus , Dito Gerry Alfa
Published: November 2024

Big data is essential in the age of 4.0 industry as it becomes the basis of decision making. Deep learning research in the last few years has been proven effective in understanding complex big data patterns, especially in the finance sector. The rapid growth of the Indonesian stock market in the last 20 years, which was driven by globalization, prompted fluctuation in the Bursa Efek Jakarta (JKSE) which was influenced by stock prices, commodity prices, and exchange rate. This study identifies the main indicators of Indonesian stock market crisis, applies and compares deep learning models, particularly Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN), in predicting stock prices. This study identified 20 JKSE crisis points between the 2002-2023 period with average return value at around -6%. All variables correlated positively with JKSE, with SET.BK as the highest correlated variable in lag 0. The American and European stock market, commodity price, and exchange rate tend to show a pattern opposite to the JKSE crisis. Predictor variables such as STI, HIS, KLSE, KS11, SET.BK, PSEI.PS, RUT, and USDIDR are chosen based on significant cross correlation and average return plot. Hyperparameter tuning and cross validation within a 3 years window concluded that the GRU model is accurate and efficient, with RMSE value at 43.35568 and MAE value at 33.66909 in the validation data.
Time Complexity of Knuth Morris Algorithm and Rejang Algorithm in Rejang-Indonesian Translator
Authors: Wibowo Sastya Hendri , Toyib Rozali , Darnita Yulia , Abadi Satria
Published: November 2024

Among the pattern-matching algorithms is the Knuth-Morris algorithm. In order to minimize the number of comparisons required and, in the worst scenario, achieve an ideal O(n+m) running time, the Knuth-Morris search algorithm skips unneeded comparisons. Every character in the text and every character in the pattern must be checked at least once by the pattern-matching algorithm. The Knuth-Morris algorithm's primary goal is to preprocess the pattern string P in order to determine the failure function f, which displays P's precise shift, so that earlier comparisons can be reused. In order to extract the fundamental word of the attached sentence, words containing affixes are separated using the Rejang stemming method. The purpose of this research is to determine the time complexity of the Rejang method and the Knuth-Morris algorithm based on affix groups. The Rapid Application Development (RAD) approach, which entails planning, designing, building, and implementing, is used during the research stages. The research results have produced efficient and effective Knuth Morris algorithm and Rejang algorithm, where efficiency is indicated by the algorithm time complexity of O (log n), and effectiveness is indicated by the accuracy results of 99% against testing 6000 affixed words.
Machine Learning Techniques for Heart Disease Prediction Using a Multi-Algorithm Approach
Authors: Biddinika Muhammad Kunta , Masitha Alya , Herman Herman , Fatimah Vita Arfiana Nurul
Published: November 2024

This analysis explores the efficiency of machine learning systems for heart disease identification through a multi-algorithm approach. The main objective is to identify the best performing algorithm for accurate disease prediction, improving clinical decision making. Using criteria including accuracy, precision, recall, F1 score, and recall, the study assessed four algorithms: Random Forest (RF), Naïve Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT). The results show that Random Forest outperforms the others, achieving 86.23% precision, 93.76% recall, 89.84% F1 score, and 88.41% accuracy. Random Forest gets an AUC ROC result of 0.94, so Random Forest is considered a superior model in this scenario, especially because it has higher accuracy. The algorithms showed a strong balance between sensitivity and specificity. Decision Tree showed reasonable performance with a precision of 84.18% and a recall of 90.27%, while Naïve Bayes recorded a precision of 87.68% and a recall of 87.03%. SVM showed a precision of 87.40% and a recall of 84.78%, indicating some limitations in capturing positive cases. The novelty of this study lies in the comparative analysis of several algorithms to optimize the heart disease prediction model for clinical use. The random forest algorithm is one of the choices, but there is still a medical standard for classifying people as either indicating or not experiencing heart failure, according to the study.
Recommendation system based on a compact hybrid user model
Authors: Sytnikova Polina E. , Hrebeniuk Mykyta O.
Published: November 2024

The object of research is the process of recommending items to the system user and improving the recommendation results. The principle of the recommendation system and the stages of creating recommendations are considered. The article describes various methods of filtering information to generate personalized recommendations. The advantages and disadvantages of each of the methods are revealed. Therefore, taking into account both theoretical and practical aspects, it is important to conduct research in order to correct the limitations characteristic of various filtering methods.
A compact hybrid user model has been proposed for the collaborative filtering method of information. This model overcomes the limitations often encountered when using traditional approaches and allows for more efficient generation of personalized recommendations. For a visual example of the described approach, the recommender system of motion pictures was taken. This model combines user ratings with descriptions of the content of objects and uses the concept of genre interest rate, which was obtained by deriving formulas for calculating a hybrid feature, that is, an indicator of how much the user is interested in a particular genre. This facilitates the formation of a set of close associates for an active user. The proposed approach is aimed at ensuring high accuracy of recommendations in the system, reducing the requirements for computing resources and effectively using information about the content of objects. The hybrid model combines the benefits of memory-based collaborative filtering and model-based recommender systems, providing accuracy and scalability.
Several examples with calculation results are given. A conclusion was drawn regarding the potential improvement of the quality of recommendations, taking into account the capabilities of the developed algorithm.
Method of automated knowledge base construction for вusiness process management information systems
Authors: Malkova Iryna A. , Makeienko Vladyslav S.
Published: November 2024

The object of research is the process of automated construction and enrichment of a knowledge base.
An approach has been defined to address the problem of automated knowledge base construction in information system development projects. Research has been conducted on models and methods for the automatic construction of a knowledge base for process management information systems. It has been determined that finite predicates, as well as predicate models describing IT project artifacts, are suitable as the mathematical apparatus.
The method for automated construction and enrichment of a knowledge base for process management information systems has been improved based on modified predicate models. The modification involves adding new predicates and aligning event logs with classes of artifacts and their properties.
The feasibility of applying the proposed method in decision support tasks under intrusion detection conditions for processes in computer systems has been verified.
The architecture and description of the main characteristics of the automated knowledge base construction system for decision-making tasks in IT enterprises, are provided.
The proposed solutions have been tested during the automated construction of a knowledge base in an IT project. Advantages and disadvantages of the method were identified during the automated construction of a production knowledge base.
Further development of the modified method may involve analyzing the success of predictions made using the knowledge base and adjusting the initial set of predicates based on the results of this analysis.
Principles of constructing mental models of a solution for external users in the task of generating explanations in an intelligent system
Authors: Chalyi Sergiy F. , Leshchynska Iryna O.
Published: November 2024

The subject of the research is the process of constructing user mental models in intelligent information systems. The aim is to improve the principles of building mental models of external users regarding the decisions of an intelligent information system to form explanations in accordance with both the needs of these users and the limitations on the use of solutions in a specific subject area. Objectives: improvement of existing principles of mental model construction based on structuring the description of the intelligent system's solution; development of a principle for constructing mental models of the intelligent system user, considering the limitations when using the obtained solution. The scientific novelty of the obtained results is as follows. The existing principles of human mental model construction have been adapted, considering the differences in constructing explanations for a solution in an intelligent system represented as a «black box». The adapted principles consider the correspondence between the structure of the mental model and the solution, the multiplicity of mental models regarding the solution, and the incompleteness of input data for decision-making. The principle of input data supplementation is proposed, which consists in using the values of solution properties that are irrelevant from the user's point of view to limit the set of relevant solutions. The use of this principle in constructing an explanation makes it possible to consider the negative aspects of using the obtained result and thereby simplify the user's choice and application of this solution.
Analyzing Instructional Strategies in Science Education: A Dual Approach Combining Systematic Review and Secondary Qualitative Data Analysis
Authors: Irmak Şeyma , Yılmaz Ergül Duygu
Published: November 2024

This study employed a dual research approach, incorporating both systematic review and secondary qualitative data analysis, to investigate instructional strategies and their rationales utilized by pre-service and in-service science teachers (PaIST) in physics topics. We chose widely recognized and readily accessible sources with extensive study coverage, including Web of Science, SCOPUS, Taylor & Francis Online, and ProQuest. Only four studies that conformed to our inclusion and exclusion criteria were identified for examination. The analysis unfolded in two phases: first, the identification of instructional strategies employed by PaIST, and second, the exploration of the underlying rationales guiding their choices. Our findings revealed a diverse array of instructional strategies, encompassing direct instruction, thought experiments, demonstrations, hands-on activities, think-pair-share, peer teaching, laboratory exercises, discussion/questioning techniques, drama, and real-life narratives. The rationales underpinning these strategies were multifaceted, aiming to enhance student motivation, stimulate cognitive development, facilitate collaborative group work, and foster meaningful learning experiences. Despite evidence supporting the effectiveness of various external strategies such as STEM education, out-of-school learning, and project-based teaching on students, pre-service and in-service science teachers appear hesitant to adopt these methods. Further research is needed to explore the barriers and factors influencing their instructional choices.
Conversational Interest between Teachers and Second Language Learners Evaluated by Humans and LLMs
Authors: Parvatham Mahathi , Tan Xingewi , Pergola Gabriele , Gambi Chiara
Published: November 2024

Stimulating language learners’ interest and engagement is essential to successful second languageacquisition, but it can be hard to translate this intuition into effective learning resources. Wecarried out the first quantitative investigation into the linguistic and pragmatic features that makean educational conversation interesting. To do so, we collected a new corpus of human interestratings for conversations between teachers and second language learners of English. Using thiscorpus, we showed that concreteness, comprehensibility (assessed via a new computationalmetric, the GIS score) and uptake (i.e., the degree to which a teacher and a student’s turn build onone another) all have unique relations to interest. This provides proof of concept that - despite thehigh degree of subjectivity involved in perceptions of interest - it is possible to extract featuresthat make a conversation interesting for the average learner. We also showed that - when a newtopic is introduced in a conversation - raters judge the corresponding turn as being moreinteresting than they expected, suggesting a role for expectations in determining interest. Finally,we evaluated the capability of Large Language Models (LLMs) to align with human ratings ofconversational interest, and showed that - while the task is difficult for all models - those modelsthat are fine-tuned on human preferences do better than those that are not. These findings provideinsights into what makes for interesting conversations between teachers and second-languagelearners of English, and lay the foundations for future work on the optimization of LLMs formore engaging language learning interactions.
Good modeling practice for calibration applied to ion exchange breakthrough prediction
Authors: Illana González Daniel , Schneider Mariane Yvonne , Gallo Juan Pablo , Nopens Ingmar , Torfs Elena
Published: November 2024

Ion exchange (IX) is a key technology in resource recovery processes for demineralization and fit-for-purpose water production due to its inherent ion-selective recovery properties. A major bottleneck in the optimization of the IX process is the accurate prediction of ion breakthrough times, which has the potential to save on regeneration chemicals by maximizing resin utilization. However, the models used to predict ion breakthrough times are often unreliable due to poor calibration methods and significant uncertainty in parameter estimates. Consequently, we conducted local and global sensitivity analyses to identify the design and operational parameters that contribute most to the prediction of breakthrough curves. The global sensitivity analysis enabled the selection of a limited subset of parameters for calibration, demonstrating that only two parameters, namely the maximum adsorption capacity isotherm parameter and the resin bead particle size, require thorough calibration, resulting in a 76% improvement in the breakthrough prediction. We also showed that the calibration of additional, less sensitive or correlated parameters results in an insignificant improvement of the predictive power, with a 16% to 60% increased uncertainty in the breakthrough time prediction. The model was validated using three independent data sets, which showed a fairly accurate breakthrough time prediction, with a relative error ranging from 1% to 11%. Herein, we propose a robust calibration procedure, based on good modeling practice, that encompasses both sensitivity and uncertainty analyses and therefore provides a basis for process optimization. The framework is presented in a manner that allows for its application to analogous process settings.
Examining the Moderating Effect of Emotion Reactivity on the Association between Momentary Negative Affect and Suicidal Thinking
Authors: Wittler Ellen M. , Bentley Kate , Nock Matthew K. , Kleiman Evan
Published: November 2024

Most theories of suicide suggest that it occurs as a result of negative affect, but surprisingly few studies test nuanced associations between negative affect and suicidal thoughts. We hypothesized that negative affect would be especially likely to predict suicidal thoughts among those with strong emotion reactivity: that is, those with greater sensitivity, intensity, and persistence of negative affect when it is experienced. We used ecological momentary assessment to examine the real-time process of how emotion reactivity interacts with affect to predict suicidal thinking. As hypothesized, across two samples (n=43 college students reporting suicidal ideation; n=52 individuals with a history of suicide attempt recruited online), results revealed that the association between momentary negative affect and suicidal thinking was stronger among those with greater trait emotion reactivity. Regarding individual facets of emotion reactivity, the interaction with intensity was significant in Study 1, whereas the interactions with persistence and sensitivity were significant in Study 2. These findings support the real-world components of the emotion reactivity model, suggesting trait-level patterns of emotional responses are important to consider in modeling and analyzing momentary mood states. Between-sample inconsistencies between which facets drove this relationship may speak to individual differences in emotional processing.
Fun at work, job engagement, and burnout: A Meta-analysis and Narrative Synthesis
Authors: Tzioti Eva , Montgomery Anthony , Spiliou Maria , Mouratidis Christos , Chalili Vilma , Maliousis Ilias , Lainidi Olga
Published: November 2024

Fun, play, and humour are accepted as integral to understanding how individuals cope with adversity at work and thrive within an organization. Far from being merely about entertainment, Fun at Work (FaW) embodies strategic elements via social bonding, essential for bolstering employee well-being, fostering engagement, and enhancing productivity. The present meta-analysis and narrative synthesis examined the relationship between FaW with work engagement and job burnout. The results of the meta-analysis showed a substantial overlap between Work Engagement and FaW and a smaller overlap with emotional exhaustion. Meta-regression indicated that the relationship between FaW and work engagement was stronger in Non-WEIRD countries and studies with samples including a higher proportion of men. The narrative review revealed two primary and bidirectional categories that capture the essence of workplace fun: Organization-Driven Fun (ODF) and Employee-Driven Fun (EDF). ODF and EDF provided the basis for a new conceptual approach to FaW outlined in the paper. The paper reflects on whether FaW might be inherently part of work engagement rather than a separate construct, functioning as a job resource.
Mental Health in the First Generation of Adults with Fanconi Anemia
Authors: Bogart Kathleen
Published: November 2024

Fanconi anemia (FA) is a rare genetic disorder involving bone marrow failure and cancer predisposition. Due to medical advances, people with FA are now living to adulthood and unmet mental health concerns are emerging. We aimed to explore mental health outcomes and protective and risk factors for the first time using a participatory research approach. Participants included 102 adults with FA from diverse national, ethnic, and racial backgrounds who completed validated measures of anxiety, depression, post-traumatic stress disorder (PTSD), health-related quality of life, FA characteristics, personal factors, and social factors. When compared to population norms, adults with FA had significantly poorer health-related quality of life on all domains measured, including anxiety, depression, fatigue, physical function, sleep disturbance, social participation, pain, and cognitive function. Results showed 50% of participants had probable PTSD, 33% had probable anxiety, and 25% had probable depression. Hierarchical regressions indicated protective/risk factors for anxiety, depression, and PTSD included stigma, disability self-efficacy, fatigue, concern with death and dying, and post-traumatic growth. Adults with FA have high rates of PTSD, anxiety, and depression. Just as people with FA should receive regular cancer screenings, they should also be screened for mental health symptoms.
L’extrême droite est-elle le porte-voix du malaise des quartiers populaires ? Le mythe des campagnes pour le logement social de Casapound en Italie
Authors: Froio Caterina , Gattinara Pietro Castelli , Vitale Tommaso
Published: November 2024

C’est désormais un poncif : l’extrême droite serait une quasi-résultante des problèmes de l’urbanisme, le fruit des classes populaires et moyennes confrontées aux difficultés de l’habitat dégradé en secteur cosmopolite. Cette étude de Caterina Froio, Pietro Castelli Gattinara et Tommaso Vitale analyse en profondeur l’action et l’implantation de CasaPound, mouvement italien considéré comme une référence et un modèle par les radicaux de droite en Europe, dans les quartiers populaires. Elle démontre empiriquement comment des actions fabriquées pour les médias occultent une réalité plus en demi-teintes.
Artificial Intelligence (AI) and the Future of (Quantitative) Social Science
Authors: Bann David , Wright Liam
Published: November 2024

This briefing note explores the transformative potential of Artificial Intelligence (AI) for quantitative social science. It provides an overview of recent advancements, outlines possible future scenarios, and examines the opportunities and challenges that AI introduces to the field. Opportunities include substantial gains in productivity across the research process: notably in data analysis, visualisation, and dissemination. Researchers could thus spend more time focusing on research and less time on administrative or repetitive tasks. Other opportunities include using AI algorithms to analyse the increasing vastness of data obtained in surveys; using AI to improve prediction; and efficiency gains in survey or experimental design/deployment. Challenges include the potential for a vast increase in low-quality publications, especially if academic incentives remain misaligned toward quantity rather than quality of research outputs, as well as the broader existential risks AI may pose. We also include a list of resources and additional reading. This brief was written jointly by the authors; readers can compare with briefs entirely written by Generative AI (GenAI) systems, and listen to an AI generated podcast based on this brief here: https://osf.io/ew6z8/. As AI reshapes research and society, social scientists are needed to understand AI's implications and deploy AI to better achieve our goals.
Eco-tourism in Nepal
Authors: Baral Chandra Kanta
Published: November 2024

Eco-tourism involves visiting unique natural habitats with the purpose of aiding conservation efforts and observing wildlife. There are several benefits of eco-tourism which include the conservation of the environment, a rise in funding by the government, cultural exchange, and support for local businesses. The principal tourism activities in Nepal are mountain climbing, trekking, visiting religious and cultural sites, rafting, bungee jumping, rock climbing, jungle safari, and so on. Ecotourism in Nepal depends on coordinated policies, integrated conservation, and sustainable community development, requiring international and grassroots collaboration to maximize benefits while conserving natural beauty. Various projects have been organized to develop eco-tourism in Nepal. Some of the major projects that have been organized are Ghalegaon-Sikles Ecotourism Project, Annapurna Conservation Area Project, Kanchenjunga Community Based Ecotourism Project, Manaslu Nature Based Ecotourism Project and Upper Mustang Biodiversity Conservation Project.
नेपालको सुदूर पश्चिमी क्षेत्रका सवाईमा प्रयुक्त सन्दर्भ
Authors: चौलागाईंं Chaulagain प्रेमप्रसाद Premprasad
Published: November 2024

यो लेख व्यवस्थापरक तथा प्रकार्यपरक व्याकरणको प्रमुख सैद्धान्तिक मान्यता ‘परिस्थितिगत सन्दर्भ’का आधारमा नेपालको सुदूर पश्चिमी क्षेत्रका सवाईमा प्रयुक्त सन्दर्भको विश्लेषणमा केन्द्रित छ । वाङ्मयका कुनै पनि विधामा प्रयुक्त सन्दर्भको विश्लेषण भाषाप्रयोगको तात्कालिक परिस्थिति र व्यापक परिस्थितिका आधारमा गर्न सकिन्छ भन्ने व्यवस्थापरक तथा प्रकार्यपरक व्याकरणको मान्यता हो । यसैले यस लेखमा पनि भाषाप्रयोगको तात्कालिक परिस्थिति र व्यापक परिस्थितिका आधारमा नेपालको सुदूर पश्चिमी क्षेत्रका सवाईमा प्रयुक्त सन्दर्भको विश्लेषण गरिएको छ । नमुना छनोट पद्धतिका आधारमा सङ्कलन गरी विश्लेषण गरिएका सवाईमध्ये ‘छोरीको विरहको सवाई’ बाट तत्कालीन समाजमा छोराछोरीका बिच भेदभाव रहेको र उक्त भेदभावको उन्मूलनबाट मात्रै नारी र पुरुष दुवैको कल्याण हुनुका साथै समग्र समाजको उन्नति हुन्छ भन्ने सन्दर्भगत अर्थ व्यक्त भएको छ भने ‘उल्टा कलिकालको सवाई’ बाट मानिसहरू सहजता, नियमनिष्ठता, मन, वचन र कर्ममा एकरूपता जस्ता मानवीय मूल्यमान्यतामा भन्दा देखावटीपन र कृत्रिमतामा रमाएका छन् र यस्तो स्थितिको अन्त्य नभई समाजको कल्याण र प्रगति हुन सक्दैन भन्ने सन्दर्भगत अर्थ व्यक्त भएको छ । यसरी सुदूर पश्चिमी क्षेत्रका सवाईले तत्कालीन समाजमा व्याप्त विकृतिको अन्त्य, मानवीय मूल्यमान्यताको संरक्षण र समाजसुधारको चाहनालगायतका विशिष्ट सामाजिक र सांस्कृतिक सन्दर्भ प्रस्तुत गरेका छन् भन्ने निचोड प्रस्तुत लेखबाट प्राप्त भएको छ । ।
Comparison between Average Lifetime Earnings of Government and Private School Teachers
Authors: Poudel Min Raj , Shrestha Nabin
Published: November 2024

Education equips individuals with skills, enabling them to earn higher incomes than those without such qualifications. However, earnings are influenced by age/experience, indicating a significant connection between education, age, and earnings. The age-earnings profile serves as a tool to understand the lifetime earnings distribution of individuals within specific education groups, aiding in retirement decisions. In this context, the study's objectives are to estimate the average lifetime earnings of government and private school teachers and compare the age-earnings profiles, before and after tax, among these groups. The study, conducted in Kathmandu district, Nepal, focuses on estimating average lifetime earnings and comparing before-and after-tax age-earnings profiles of government and private school teachers. The post-positivism philosophy was adopted, using a cross-sectional survey design with 100 school teachers as the study unit. Results reveal differences in earning patterns between private and government school teachers, indicating that higher education correlates with higher earnings in both settings. Notably, private school teachers earn less than their government counterparts, suggesting disparities in salary facilities between Kathmandu's two types of schools.