bluuebunny/crossref_metadata_2025_split huggingface dataset:
Personalized Web Services Composition Using Artificial Intelligence
Authors: Chebbi Imen , Abidi Sarra , Ayed Leila Ben 
Published: May 2024

Abstract

The process of creating new services by integrating existing ones is known as service composition. It is regarded as one of the most difficult tasks in a distributed and dynamic setting. In general, the composition process entails looking for existing services in a certain domain, selecting the suitable service, coordinating composition flow, and activating services. Researchers have been studying the need of web service composition using artificial intelligence extensively over the last few years. As a result, numerous solutions and novel approaches to addressing this issue are given. Our goal in this research is to use artificial intelligence to personalize web services composition. In our case, We employed natural language processing (NLP) to examine the descriptions and functionality of existing online services. Based on the user profile, AI algorithms can recommend and select appropriate services that meet the user’s needs. The experimental study shows that the proposed model can achieve robust results. In fact, our solution achieves 99.45% accuracy and 99.74% Precision.
Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking approach
Authors: Heidary Gholamreza , Afrookhteh Meysam , Khaloo Hooman , Mousa Ramin 
Published: May 2024

Abstract

Digital currencies have become popular in the last decade due to their non-dependency and decentralized nature. The price of these currencies has seen a lot of fluctuations at times, which has increased the need for prediction. As their most popular, Bitcoin(BTC) has become a research hotspot. The main challenge and trend of digital currencies, especially BTC, is price fluctuations, which require studying the basic price prediction model. This research presents a classification and regression model based on stack deep learning that uses a wavelet to remove noise to predict movements and prices of BTC at different time intervals. The proposed model based on the stacking technique uses models based on deep learning, especially neural networks and transformers, for one, seven, thirty and ninety-day forecasting. Three feature selection models, Chi2, RFE and Embedded, were also applied to the data in the pre-processing stage. The classification model achieved 63% accuracy for predicting the next day and 64%, 67% and 82% for predicting the seventh, thirty and ninety days, respectively. For daily price forecasting, the percentage error was reduced to 0.58, while the error ranged from 2.72% to 2.85% for seven- to ninety-day horizons. These results show that the proposed model performed better than other models in the literature.
Valorization of rice straw via torrefaction and its effect on the physicochemical properties
Authors: Rafey Abdul , Pal Kunwar , Pant Kamal Kishore 
Published: May 2024

Abstract

The surplus biomass residue generated from biomass harvesting has enough potential for generating bioenergy and is a promising energy source for future use. Biomass possesses a high moisture content and low calorific value and therefore needs improvement to convert it into solid biofuel. In the present study, torrefaction of lignocellulosic biomass (rice straw) was carried out to enhance its physicochemical characteristics for producing high-grade biofuels and chemicals. For three sets of temperatures (200, 250, and 300°C) and residence times (30 minutes, 45 minutes, and 60 minutes), experiments were conducted in a batch reactor at a heating rate of 10°C.min− 1 in an inert environment. The torrefied products obtained were analyzed using various analytical techniques, such as proximate and ultimate analysis, calorific value measurement, and FTIR analysis. The results revealed that torrefaction at a mild temperature of 200°C and 30 minutes of residence time resulted in a maximum mass yield of 87% and an energy yield of 97%, which subsequently decreased at higher temperatures. The calorific value increased with increasing torrefaction temperature, with a maximum value of 19.50 MJ.kg− 1 occurring at 300°C and 60 minutes of residence time. Since H2O, CO, and CO2 are released upon torrefaction, a significant decrease in the number of hydroxyl groups was observed in the FTIR spectra. Despite the high calorific value at 300°C, 250°C and 30 minutes of residence time are the optimum torrefaction conditions for rice straw due to the significant mass and energy yield and the significant presence of amorphous carbon, as confirmed by Raman spectroscopy. This study will improve the physicochemical properties of rice straw for the production of high-value fuels, chemicals, and other high-strength materials.
FashionGen: AI driven fashion designing using GANss
Authors: U Manyatha , B Shreya , P Shraddha , Akshitha M , B Aruna Kumara 
Published: May 2024

Abstract

fashion image manipulation poses challenge in image transformation involving the integration of chosen clothing items into an input image traditional approaches typically rely on example images of the desired clothing design transferring them onto the target person a method known as virtual try-on in contrast this study delves into the realm of fashion image manipulation using textual descriptions offering advantages such as obviating the need for example images and enabling a broad spectrum of concepts through text however existing text-based editing techniques often face limitations due to the requirement for extensively annotated training datasets or their restricted capability to handle simple text descriptions to address these challenges we propose fashiongen fashion image rygeneration via text an innovative text-based manipulation model fashiongen augments the conventional gan-inversion by incorporating semantic pose-related and image-level constraints to generate desired images leveraging pretrained clip models fashiongen effectively imposes targeted semantics furthermore we introduce a latent-code regularization technique to enhance control over image fidelity and ensure synthesis from a well-defined latent space comprehensive experiments conducted on a dataset amalgamating viton images and fashion-gen text descriptions alongside comparisons with existing editing methods affirm fashiongens proficiency in generating realistic design images with superior transformation performance
Efficient Compression of Large Language Models: A Case Study on Llama 2 with 13B Parameters
Authors: Konishi Mariko , Nakano Kentaro , Tomoda Yoko 
Published: May 2024

Abstract

Efficient compression of large language models is important for enhancing computational efficiency and reducing the necessary virtual memory requirements for deployment in resource-limited environments. Employing a combination of advanced compression techniques—pruning, quantization, and knowledge distillation—this investigation explores the viability of reducing model size while maintaining high performance levels. Pruning reduces the number of non-critical parameters, significantly lowering the computational load. Quantization decreases the precision of the numerical data within the model, thus reducing the memory footprint without substantially affecting the model's ability to process data effectively. Knowledge distillation involves training a smaller, more compact model to replicate the predictive performance of the larger model, ensuring minimal loss of capability. The results demonstrate that the application of these techniques not only considerably enhances the speed of data processing but also reduces the operational costs and energy consumption, thereby supporting the deployment of powerful AI models in a wider variety of settings. The exploration confirms that while there is a slight reduction in accuracy and F1-score, the trade-offs are justifiable given the benefits gained in processing efficiency and resource utilization. By meticulously optimizing the balance between compression intensity and model performance, tailored solutions can be developed to meet specific operational needs, thereby expanding the practical applications of large language models in real-world scenarios. The outcomes underscore the transformative potential of model compression technologies to advance the field of artificial intelligence by enabling more sustainable and efficient AI deployments across diverse computational environments.
Evaluating Bioremediation Strategies on Microbial Diversity in Crude Oil-Contaminated Soil Over Three to Six Months in Port Harcourt, Nigeria.
Authors: Evans Fubara Gift , Nkalo Ukoima Hudson , Amachree Dokuboba , Raimi Morufu Olalekan 
Published: May 2024

Abstract

Rationale: The study aimed to evaluate the efficacy of various bioremediation approaches on microbial diversity in crude oil-contaminated soil over a duration of three to six months in Port Harcourt, Nigeria.
Objective: The objective was to assess the impact of different bioremediation strategies on microbial populations, particularly focusing on hydrocarbon-utilizing bacteria and fungi.
Method(s): Microbial populations were quantified using serial dilution and microbial count techniques. The vapor phase transfer mechanism was employed to estimate hydrocarbon-utilizing bacteria and fungi. Bacterial and fungal colonies were incubated for five days, followed by biochemical tests for isolate identification. Fungal pure cultures were observed under a microscope.
Results: The study observed a significant increase in microbial populations in soil free of crude oil pollution when bioremediators such as mushrooms and earthworms were introduced. Mushrooms exhibited a 50% increase in hydrocarbon-utilizing bacteria (HUB), while earthworms showed a 55% increase in HUB over the three to six-month period. The longer lifespan and nutrient absorption capabilities of earthworms facilitated faster growth. Furthermore, significant growth in the microbial population of hydrocarbon-utilizing bacteria and fungi was observed in crude oil-polluted soil after employing bioremediators, with the highest growth observed in soil treated with mushrooms at six months, followed by earthworms at six months. Conversely, the lowest microbial population was recorded in soil polluted with 10% crude oil and remediated with earthworms at three months.
Conclusions: The results suggest that both mushrooms and earthworms effectively increase microbial populations in crude oil-polluted soil. However, mushrooms demonstrated a higher microbial population increase compared to earthworms, especially in terms of promoting the growth of hydrocarbon-utilizing bacteria (HUB) and hydrocarbon-utilizing fungi (HUF).
Recommendations: Based on the findings, it is recommended to prioritize the use of mushrooms as bioremediation agents in similar environmental restoration efforts due to their superior efficacy in increasing microbial populations, particularly HUB and HUF.
Significance Statement: This study underscores the potential of mushrooms and earthworms as effective bioremediation agents for restoring microbial diversity in crude oil-contaminated soil, offering insights for sustainable environmental restoration practices in oil-affected regions like Port Harcourt, Nigeria.
Impact of Exchange Rate Volatility on the Manufacturing Sector Growth Performance in Nigeria
Authors: M Abiola Olawale 
Published: May 2024

Abstract

This study investigated the impact of exchange rate volatility on the manufacturing sector's growth performance in Nigeria from 1987 to 2022. Employing the Autoregressive Distributed Lag (ARDL) technique, the study utilized the Augmented Dickey Fuller (ADF) test to assess the stationarity of the variables. The regression analysis revealed a partial effect of the exchange rate on the manufacturing sector's growth. However, the study concluded that exchange rate volatility insignificantly influenced Nigeria's manufacturing sector throughout the examined period. It suggests that other factors, including government policies, global economic conditions, and domestic market dynamics, play pivotal roles in shaping the overall growth performance of the manufacturing sector.
A síndrome de Burnout em Universitários e as Contribuições da Terapia Cognitivo Comportamental
Authors: Keller da Silva Ana , Lacerda Vilela Luana , Duarte Antão Sandra 
Published: May 2024

O presente artigo elucida os impactos do estresse na saúde mental de universitários destacando a síndrome de Burnout como uma das consequências encontradas nesse contexto. A metodologia utilizada foi a pesquisa bibliográfica, onde foram selecionados estudos que permitiram discorrer compreender como a Síndrome de Burnout pode afetar a vida do estudante universitário e apresentar quais práticas de saúde mental segundo a abordagem Cognitivo Comportamental podem auxiliar de forma preventiva. A estrutura tradicional de ensino foi apontada como contribuinte para o surgimento e agravamento do burnout, sendo avaliada a importância de repensar o modelo de gestão das instituições de ensino superior, assim como a possibilitar de ofertar suporte emocional e psicológico adequado aos estudantes. É apontado ainda que os estudantes possam aprender a identificar seus próprios limites e estabelecer uma rotina saudável que inclua tempo para descanso, lazer e autocuidado.
Mapeamento do registro do processo de enfermagem em idosos residentes na ILPI com covid-19
Authors: Rosa Cristiane dos Santos , Santana Rosimere Ferreira , Bitencout Graziele Ribeiro 
Published: May 2024

Introdução: As instituições de longa permanência para idosos (ILPI) são modalidades de cuidados especializados que tendem a crescer devido ao envelhecimento populacional, a participação das mulheres no mercado de trabalho e redução do núcleo familiar. O contexto da pandemia da covid-19 aumentou a visibilidade dessas instituições, principalmente, no que tange aos registros das atividades profissionais. Objetivo: Este estudo objetivou identificar. Qual a produção bibliográfica disponível sobre o processo de enfermagem nas ILPIs no geral e no contexto da Covid-19. Materiais e métodos: Trata-se de uma revisão integrativa da literatura realizada nas bases de dados MEDLINE, LILACS e CINAHL, conforme a metodologia Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A amostra constituiu-se de quatro artigos envolvendo a temática. Os estudos incluídos eram de países como Suecia, Islovenia e Brasil. Considerações finais: Evidencia-se a necessidade de aumento da produção de pesquisa sobre a temática na área da enfermagem. Sendo assim, se faz necessário a reflexão da documentação da prática de enfermagem, no sentido de dar visibilidade para o cuidado ao idoso no contexto da Covid-19, no que tange a abordagem dos registros do processo de enfermagem específico para as ILPIs contexto da pandemia e principalmente no contexto brasileiro, evidenciando a lacuna no conhecimento. Sendo assim, se faz necessário a reflexão da documentação da prática de enfermagem, no sentido de dar visibilidade para o cuidado ao idoso no contexto da COVID-19.
Palavras-Chave: COVID-19; Instituição de Longa Permanência para Idosos; Processo de Enfermagem.
Percepção dos profissionais da saúde sobre o trabalho durante a pandemia de COVID-19
Authors: Rocha Rejane Silva , Rodrigues Luiz Antônio da Costa , Oliveira Danielle Furtado de , Santos Wilkens Barbosa dos , Motta Fernando Couto , Peiter Paulo Cesar 
Published: May 2024

Introdução: A pesquisa teve como objetivo verificar a percepção dos profissionais da saúde sobre o trabalho durante a pandemia de COVID-19. Materiais e Métodos: Pesquisa do tipo qualitativa, com coleta de dados em dois hospitais do município do Rio de Janeiro, um da rede privada e outro municipal público. Envolveu 23 profissionais da saúde sendo obtida por meio de entrevistas semiestruturadas. Resultados: Com a análise de conteúdo emergiu 4 categorias: Resiliência Profissional: Superando a Sobrecarga no trabalho; Estratégias para minimizar o Risco Biológico no trabalho; Produtividade e Saúde Mental e Aproximação e solidariedade com colegas de trabalho. Discussão: Os profissionais passaram por diversos desafios ao longo da pandemia, ressalta-se a importância do apoio entre as equipes e adequação das atividades para minimizar os dados a saúde desses. Considerações finais: As experiências compartilhadas pelos profissionais da área da saúde mostraram como a pandemia impactou suas rotinas de trabalho, vida pessoal e relacionamentos.
Palavras-Chave: Saúde do Trabalhador; Saúde Mental no Trabalho; Pandemias; Infecção por Coronavírus; Pessoal de Saúde.
Ứng dụng phân loại bằng máy vector hỗ trợ trong đánh giá tính tích cực học tập của sinh viên
Authors: Lê Khánh Duy , Nguyễn Ngọc Đăng Khoa , Trương Huỳnh Kỳ , Huỳnh Phước Nghĩa , Lê Phương Thảo 
Published: May 2024

Bài toán phân loại đã được vận dụng trong nhiều lĩnh vực khác nhau, đặc biệt trong y học. Thái độ học tập tích cực là một trong những yếu tố chủ chốt quyết định trực tiếp đến kết quả học tập của sinh viên trong môi trường học tập tự chủ ở bậc đại học. Nghiên cứu này xây dựng bộ dữ liệu đánh giá tính tích cực trong học tập, sau đó áp dụng mô hình phân loại hiệu quả cho bộ dữ liệu này và đề xuất mô hình phân loại để chẩn đoán kịp thời "bệnh chán học" cho cá nhân sinh viên, từ đó có các biện pháp hỗ trợ kịp thời.
Analisa Pengetahuan Keuangan dalam Mempengaruhi Perilaku Manajemen Keuangan dengan Pendapatan sebagai Faktor Moderasi
Authors: Dewanti Made Amanda , Yulianthini Ni Nyoman , Suarmanayasa I Nengah , Heryanda Komang Krisna 
Published: May 2024

This study aims to determine the relationship between financial knowledge, income and financial management behavior. Income acts as a moderating variable in this study. The subjects in this study were private employees in Sawan Village. The number of samples used in this study was 100 people with proportional random sampling techniques. Data analysis using structural equation model (PLS-SE). The result of this study is that income cannot strengthen the influence of financial knowledge on financial management behavior, so it is advisable to reduce excessive consumptive behavior, start developing financial management behaviors such as making budgets and saving, and be able to control your own financial funds , the result of these two studies is that financial knowledge has an influence on financial behavior and the last research result is that opinions have an influence on financial management behavior.

Keywords : financial knowledge, income, financial management behavior
ANALISIS KINERJA KEUANGAN DENGAN MENGGUNAKAN METODE ECONOMIC VALUE ADDED (EVA) PADA PERUSAHAAN SEKTOR MAKANAN DAN MINUMAN YANG TERDAFTAR DI BURSA EFEK INDONESIA (2019 - 2022)
Authors: Yanto I Putu Indra Yupri , Astawa I Gede Putu Banu 
Published: May 2024

Penelitian ini bertujuan mengetahui kinerja keuangan perusahaan sektor makanan dan minuman yang terdaftar di Bursa Efek Indonesia. Metode penelitian yang digunakan yaitu metode kuntitatif dengan pendekatan deskriptif. Metode penelitian yang digunakan yaitu metode kuntitatif dengan pendekatan deskriptif. Adapun teknik pengumpulan data yang digunakan yaitu www.idx.co.id dan situs resmi perusahaan terkait, berupa laporan tahunan perusahaan makanan dan minuman yang terdaftar di BEI selama tahun 2019-2022. Hasil perhitungan Economic Value Added pada 19 perusahaan makanan dan minuman yang menghasilkan nilai Economic Value Added dari tahun 2019 sampai tahun 2020 bernilai positif atau (EVA > 0) terdapat 10 perusahaan yang hasilnya berfluktuasi tetapi dinyatakan positif setiap tahunnya, maka telah terjadi penambahan nilai ekonomis bagi perusahaan tersebut sehingga kinerja keuangan dapat dinyatakan dengan baik. Sedangkan ada 9 perusahaan yag setiap tahunnya terdapat nilai (EVA < 0) meskipun ada beberapa hasil EVA yang diperoleh negatif.
Pengaruh Volatilitas Arus Kas, Volatilitas Penjualan, Book Tax Difference, Tingkat Hutang Dan Kepemilikan Institusional Terhadap Persistensi Laba
Authors: Lovita Ajeng , Lisiantara Anggana 
Published: May 2024

Penelitian ini bertujuan untuk menganalisis pengaruh volatilitas arus kas, volatilitas penjualan, book tax difference, tingkat hutang dan kepemilikan institusional terhadap persistensi laba. Persistensi laba didefinisikan sebagai laba yang dapat digunakan sebagai indikator laba di masa yang akan datang, dengan kata lain persistensi yaitu kemampuan laba suatu perusahaan untuk bertahan di masa depan. Variabel dependen yang digunakan dalam penelitian ini adalah persistensi laba pada tahun pengamatan 2019-2021 Populasi pada penelitian ini adalah 155 perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia. Data penelitian diperoleh dari laporan keuangan perusahaan manufaktur periode tahun 2019-2021. Berdasarkan metode purposive sampling, sampel yang diperoleh sebanyak 32 perusahaan. Hipotesis dalam penelitian ini diuji menggunakan analisis regresi linier berganda. Hasil penelitian ini menunjukan bahwa volatilitas arus kas, tingkat hutang, kepemilikan institusional berpengaruh positif dan tidak signifikan terhadap persistensi laba. Sedangkan volatilitas penjualan berpengaruh negatif dan tidak signifikan terhadap persistensi laba. Namun book tax difference berpengaruh negatif dan signifikan terhadap persistensi laba.

ABSTRACT

This research aims to analyze the affect of cash volatility, sale volatility, book tax difference, leverage and institutional ownership on earning persistence. Earnings persistence is defined as the profit that can be used as an indicator of future earnings, in other words, the persistence of the earnings of a company's ability to survive in the future. The dependent variabel used in this study is earning persistence in observations 2019-2021.
The population of this research is 155 companies in the manufacturing sector which were listed in Indonesian Stock Exchange (IDX). The research data were collected from manufacturing companies financial statement for the period of years 2019-2021. Based on purposive sampling method, there are 32 samples. The reseacrh hypotesis were tested using multiple linear regression analysis.
The results indicate that the cash volatility, leverage, institutional ownership firm has positive and no significant effect on earning persistence. While sales volatility firm has negative and no significant effect on earnings persistence. But the book tax difference firm has negative and significant effect on earnings persistence.

Key words : Earning persistence, cash volatility, sales volatility, book tax difference, leverage and institutional ownership
Determinan Penggunaan QRIS Oleh UMKM Di Kecamatan Buleleng
Authors: Sari Ni Made Putri Candra , Yudantara I Gede Agus Pertama 
Published: May 2024

Tujuan penelitian ini yaitu untuk mengetahui Determinan Penggunaan Qris Oleh Umkm Di Kecamatan Buleleng melalui variabel Persepsi Manfaat , Persepsi Kemudahan, Norma Subjektif Dan Literasi Keuangan. Rancangan penelitian ini adalah penelitian kuantitatif. Jenis data yang digunakan dalam penelitian ini adalah data kuantitatif merupakan data dalam bentuk angka dan dinyatakan dalam satuan hitung, data yang diperoleh dari hasil penyebaran kuesioner berupa skor angka. Sumber data dalam penelitian ini yaitu dana skunder dan data primer. Populasi yang digunakan adalam penelitian adalah UMKM di Kecamatan Buleleng. Metode penentuan sampel menggunakan pemilihan sampel jenuh. Jumlah responden dalam penelitian ini 97 UMKM yang menggunakan QRIS di Kecamatan BulelengHasil uji menyatakan bahwa variabel persepsi manfaat berpengaruh positif terhadap penggunaan QRIS, variabel persepsi kemudahan berpengaruh positif terhadap penggunaan QRIS, variabel norma subjektif berpengaruh positif terhadap penggunaan QRIS dan variabel literasi keuangan berpengaruh positif terhadap penggunaan QRIS di Kecamatan Buleleng. Serta dalam uji koefisien determinasi (R2) menunjukkan hasil bahwa variabel variabel Persepsi Manfaat , Persepsi Kemudahan, Norma Subjektif Dan Literasi Keuangan mampu mempengaruhi Penggunaan Qris Oleh Umkm Di Kecamatan Buleleng secara bersama-sama sebesar bahwa 78,7% sedangkan sisanya sebesar 21,3% dipengaruhi oleh variabel lain diluar regresi.
Kata Kunci: Persepsi Manfaat, Persepsi Kemudahan, Norma Subjektif, Literasi Keuangan, Penggunaan QRIS.