bluuebunny/crossref_metadata_2025 huggingface dataset:
COMPUTATIONAL STUDIES AND GREEN SYNTHESIS OF BENZIMIDAZOLES THROUGH SEQUENTIAL OXIDATIVE CYCLISATION OF ARYLDIAMINES IN SOLVENT-FREE CONDITIONS
Authors: Pandey Alka , Pandey Jaya , Dubey Ragini , Kant Ravi
Published: September 2024

Benzimidazoles were synthesized under solvent-free conditions using Ionic Liquids (IL), specifically 1-Pentyl-3- methylimidazolium bromide ([pmim]Br), derived from aryl aldehydes and phenylenediamine. A comparative study of yields was conducted by reacting aryl aldehydes with both electron-donating and electron-withdrawing groups with substituted o-phenylenediamines, resulting in the formation of 2-phenylbenzimidazoles via cyclization. The yield of final products, optimum reaction conditions, plausible mechanism of the reaction, and the effect of substitutions at various positions of either of the reactants are presented in this paper. Computational studies have been done to explore the possibility of synthesized compounds for anticancer activity. Molecular docking studies were conducted to explore the inhibitory effects of steroidal derivatives against the estrogen receptor-derived Mutants [PDB ID: 2ITO]. The docking results of all compounds revealed significant inhibitory activity against the estrogen receptor 2ITO, with binding energy (ΔG) values for compounds 3a, 3b, 3c, 3d, 3e, 3f, and 3g against protein 2ITO recorded as -7.1 Kcal/mole, -7.3 Kcal/mole, -7.2 Kcal/mole, -7.8 Kcal/mole, -7.5 Kcal/mole, -7.6 Kcal/mole, and -7.6 Kcal/mole respectively.
Free vibration analysis of laminated composite shallow curved beam with various boundary conditions
Authors: Nhan Nguyen Thien , Duong Nguyen Ngoc , Kien Nguyen Trung
Published: September 2024

This study introduces a novel Ritz approximation function to analyze the free vibrations of laminated compositeshallow curved beams. The methodology utilizes polynomial functions-based Fibonacci series to develop theRitz’s approximation function. The displacement field aligns with the high-order shear deformation theorydesigned explicitly for shallow curved beams. The problem’s governing equation is established by applying theLagrange equation, providing a comprehensive framework for the subsequent analysis. The study meticulouslyexamines the convergence rate and accuracy of the proposed approximation function. Numerical investigationsare conducted to determine the natural frequencies of curved beams. Factors such as curvature-to-length ratio,length-to-thickness ratio, boundary conditions, and material anisotropy are carefully considered. Furthermore,extensive survey results concerning the natural frequencies of curved beams are presented. These results serveas valuable reference data for future investigations in this area, thereby contributing to the academic discourseon this subject matter.
Effects of cement paste viscosity on the properties of lightweight expanded polystyrene concrete
Authors: Thang Nguyen Cong , Tuan Nguyen Van
Published: September 2024

Lightweight expanded polystyrene concrete (EPS-C) offers several advantages, including low density, sound resistance, and good thermal insulation. These characteristics are contributed by the use of expanded polystyrene (EPS) with a closed cellular structure, which is non-absorbent, hydrophobic, and low-density (around 6.9 kg/m³). Since EPS is much lighter than cement paste, the viscosity of the paste plays an important role indirectly affecting segregation and the properties of EPS-C mixtures. This paper presents the experimental results of the viscosity and its influence of cement paste on both the segregation of the concrete mixture and thecompressive strength of EPS-C. The research revealed that a viscosity of cement paste below 50 mPa.s resultsin segregation of the concrete mixture. As the viscosity increases, the degree of segregation decreases. However, when the viscosity exceeds 180 mPa.s, using a viscosity-modifying admixture becomes a good solution to prevent segregation in EPS-C mixtures. Therefore, the optimal viscosity range for the binder paste to ensure the concrete mixture does not segregate is between 50 and 180 mPa.s.
A MITC3+ element improved by edge-based smoothed strains for free vibration and buckling analyses of porous plates based on the first-order shear deformation theory
Authors: Le-Phuong Binh , Chau-Dinh Thanh
Published: September 2024

This paper presents the free vibration and critical buckling analyses of porous plates based on the first-ordershear deformation theory by the proposed three-node triangular element. The bending strain fields of the suggested element are enriched by the bubble node located at the centroid of the triangular element. The shear locking phenomenon is eliminated by the independent interpolations of the transverse shear strains following the MITC3+ technique. The edge-based smoothed (ES) strain method is employed to improve the in-plane strain fields. The influence of the porosity distributions, length-to-thickness ratios, porous coefficient, and boundary conditions on the free vibration and critical buckling load of the porous plates are evaluated through several numerical examples by the proposed element, namely ES-MITC3+ element. The obtained results are compared with other references to perform the efficiency of the proposed element.
Cost optimization in structural design for reinforced concrete frames using Jaya algorithm
Authors: Phan Hao Dinh , Phan Sang Van
Published: September 2024

This study analyzes and optimizes the structural design of three-dimensional (3D) reinforced concrete framestructures to minimize the amount of two main materials, including concrete and steel reinforcement, used inreinforced concrete frames. Jaya algorithm was developed based on evolutionary algorithms to build the structural optimization model. The objective function (minimum material cost) with variables being column andbeam cross-sections was set up. In which, the constraints related to the maximum internal force and displacement being in the structural members satisfy limited strength and serviceability requirements. A subroutine written in Python was used to connect the optimization process to the structural analysis software, ETABS. The 3D reinforced concrete frame of a three-story building was selected for cost and design optimization analysis. In which, the optimization problem was solved in cases of three different maximum numbers of iterations, including 20, 35, and 50. In each iteration level, a setting of five independent runs was performed. The subroutine proved to be fast, robust, and convenient manner for solving optimization problems in designing 3D reinforced concrete frames. In which, the best optimal cost might be obtained after twenty iterations and the better convergence behavior occurred at higher numbers of iterations. The study results showed that, for this reinforced concrete frame, applying optimal design led to a materials cost reduction (success rate) by 33.67% compared to that of the conventional design. This success rate would fluctuate according to different nations’ design codes.
Peran Teknologi Dalam Proses Pembelajaran Di SD Negeri 112 Palembang
Authors: Faridah  , Galuh Meilyn Asriani  , Ihda Hilyati  , Syarifuddin
Published: September 2024

Penelitian ini bertujuan untuk mengetahui peran teknologi dalam proses pembelajaran yang ada di SD Negeri 112 Palembang. Metode penelitian yang dilakukan dalam penelitian ini adalah metode penelitian kualitatif deskriptif. Teknik pengumpulan data yang dilakukan oleh peneliti adalah wawancara terhadap para guru SD Negeri 112 Palembang. Hasil dari penelitian ini adalah para guru  sudah mengikuti perkembangan zaman dengan menerapkan teknologi dalam proses pembelajaran.
kata kunci : Teknologi pendidikan, pembelajaran
Biodegradation of n-Alkanes in Oil-Contaminated Bottom Sediments under Bioelectrochemical Stimulation
Authors: Samkov A. A. , Volchenko N. N. , Musorina T. N. , Kruglova M. N. , Samkova S. M. , Khudokormov A. A.
Published: September 2024

Degradation of oil hydrocarbons artificially introduced into bottom sediments in a bioelectrochemical system of a membrane-free (silt) type was studied. Passive bioelectrochemical stimulation by means of electrodes connected by an external circuit with a resistance of 1 kΩ, with an average electric current of ~85 µA was found to cause an increase in degradation during two months from 23.0 to 57.9%. Contamination of bottom sediments with oil (1.32 g/kg) slightly decreased the current in the external circuit of the bioelectrochemical system. The relationship was revealed between the degree of oil degradation and predominant utilization of the lighter n-alkanes in the C14H30–C30H62 series, compared with both the original oil and the residual hydrocarbons of the control. An increase in the representation of the alkB alkane monooxygenase genes relative to the 16S rRNA gene in the total DNA isolated from the sediments was induced by the introduction of hexadecane, both in the case of electrochemical stimulation and in the control. The results may be of interest for the development of new methods of bioelectrochemical removal of organic pollutants from anaerobic environments.
Antimicrobial Activity of Terpenes and Oxygen-Containing Terpenoids Against Staphylococcus aureus
Authors: Kolesnikova A. I. , Kayumov A. R. , Gilfanov I. R. , Frolova L. L. , Nikitina L. E. , Trizna E. Yu.
Published: September 2024

At various pathological states, Staphylococcus aureus may cause a number of diseases. Rapid spread of antibiotic resistance necessitates the search for alternative approaches to antimicrobial treatment of staphylococci. Due to their membranotropic properties, natural and synthetic terpenoids are capable both of antimicrobial action and of potentiation of the effect of other antimicrobial agents. Antimicrobial activity of 53 natural terpenes and their synthetic oxygen-containing derivatives was determined, as well as their ability to potentiate the effect of known antimicrobials on S. aureus. Two- to fourfold increase in efficiency of amikacin, ceftriaxone, and miramistin in the presence of (+)-3β,4β-carandiol and (‒)-myrtenic acid indicates the possibility of their application for combined therapy together with medical preparations in order to decrease the concentrations of the latter.
Seismic velocity changes beneath Ebeko Volcano (Kuril Islands) based on observations in July-August 2021
Authors: Berezhnev Ya. M. , Belovezhets N. N. , Koulakov I. Yu. , Jakovlev A. V. , Alajmi M. S. , Gordeev E. I.
Published: September 2024

Here we present seismic velocity changes in the northern part of Paramushir Island, where presently active Ebeko Volcano is located. We use the data of a seismic network that operated in this area in 2021–2022 during a continuous eruption activity of Ebeko. We selected the data from July 1 to August 15, when most stations of the network provided prompt recording of seismic signals. The velocity changes were derived from the ambient noise cross-correlation functions. To identify common features in the obtained velocity variation curves, we have performed the cluster analysis and separated all the curves in two groups having similar shapes within each group, but strongly different between the groups. Velocity changes in Cluster 1 corresponding to the station pairs covering the entire Northern Paramushir have some delayed correlation with the precipitation intensity, implying that these changes might be caused by meteoric fluid migration. In Cluster 2, which is more concentrated in the area of Ebeko Volcano, the velocity changes are apparently connected with both external factors (precipitation and atmospheric pressure) and internal indicators of volcano activity (phreatic explosions, seismicity, thermal anomalies and gas emission).
The global process of eutrophication and its features in Arctic lakes as a consequence of climate warming
Authors: Moiseenko T. I. , Bazova M. M.
Published: September 2024

An analysis of the problem of water bodies eutrophication as a global process is presented. The volumes increasing use of nitrogen and phosphorus on a planetary scale are shown, the dispersion of which leads to an increase in the content of nutrients in lakes and rivers. The results of original studies of remote lakes in the Arctic zone are presented, which showed an increase in the concentrations of nutrients in lakes in recent decades. A tendency has been revealed for an increase in the contents of nitrogen and phosphorus, as well as organic matter in lake waters, even in the absence of anthropogenic influence. It has been established that an increase in temperature and climate warming in the Arctic regions exert the main influence on the increase in the content of nutrients and the trophic status of lakes.
FORMATION OF A SYSTEM OF GOALS FOR MANAGING THE INNOVATION ACTIVITIES OF AN ENTERPRISE IN THE CONDITIONS OF POST-CONFLICT TRANSFORMATION
Authors: IVCHENKO Yuliia
Published: September 2024

The article examines the role of innovation in the development of enterprises. An approach to innovation management at an enterprise in the context of post-conflict transformation is developed, based on the premise that the goals of an enterprise should determine the goals of innovation management, and the processes of innovation management at an enterprise should be integrated with the vertical and horizontal levels of economic and production business processes in the context of post-conflict transformation. Based on the results of the study, the mathematical basis for innovation management in the system of methodological approaches (transfer management, algorithmic management, creative management) based on the identified processes of innovation management at enterprises in the context of post-conflict transformation is determined. An analysis of approaches to the formation of goals for managing innovations at enterprises in the context of post-conflict transformation is carried out. The approach, which is contained within 8 key spaces, with the allocation of 5 groups of goals (financial, production, marketing, human resource management, information and technical support) is substantiated. A system of goals for managing the innovation and investment activities of enterprise is formed. The author details managerial actions/decisions at the strategic, tactical and operational levels and specifies tactical functional goals in the context of financial, production, marketing, human resource management and information technology, which ensures further optimization of the use of various types of resources (primarily human resources) at the enterprise. A mathematical framework has been developed for the subsystem at the strategic, tactical and operational levels. The obtained results can be implemented in the activities of enterprises and used in further scientific research.
Infographic: On-site provision of family planning services in factory settings in Egypt [in Arabic]
Authors: Abdel-Tawab Nahla , Abou El-Fadl Reham , El Khorazaty Noha , Alam Eldin Tarek
Published: September 2024

Under the project titled "Offering Family Planning Services in Factory Settings in Egypt: Examining Feasibility, Acceptability, and Cost," this set of infographics describe the intervention conducted in Alexandria, Egypt, aimed at addressing male and female workers' needs for family planning and reproductive health (FP/RH) information and services. These infographics provide an overview of the intervention components, the results of the quantitative evaluation, as well as the cost and return on investment analysis.
Respon Generasi Zilenial dan Milenial Terhadap Minat Pembelian Minuman Serbuk Kulit Mangga
Authors: Fadilah Arsy Nur
Published: September 2024

Penelitian ini bertujuan untuk mengidentifikasi minat pembelian generasi Zilenial dan  Milenial terhadap produk minuman serbuk kulit mangga. Responden pada penelitian ini berjumlah 30 orang dengan rentang usia 17-25 tahun yang diambil secara aksidental siapa saja yang kebetulan bertemu dengan peneliti. Metode yang digunakan yaitu metode eksperimental dan survei dimana responden mencoba langsung konsumsi produk serbuk kulit mangga dan mengisi kuesioner yang telah disediakan. Analisis data digunakan untuk mengidentifikasi minat pembelian minuman serbuk kulit mangga  meliputi sebagai berikut: 1) Keinginan untuk mencaritahu informasi mendalam tentang produk minuman serbuk kulit mangga. 2) Keinginan untuk membeli produk minuman serbuk kulit mangga. 3) Keinginan memilih produk minuman serbuk kulit mangga dibanding produk sejenisnya. 4) Keinginan untuk merekomendasikan serbuk kulit mangga kepada kerabat dan keluarga. Hasil penelitian menunjukkan Sebesar 66% responden setuju untuk mencari informasi lebih tentang produk , Sebesar 66,7% responden setuju untuk membeli produk. . Sebesar 50,0% responden setuju untuk memilih produk dibandingkan dengan produk lain yang sejenis.  , Sebesar 56,7% responden setuju untuk merekomendasikan produk kepada kerabatdan keluarga. Minuman serbuk kulit mangga dianggap sangat menarik , sangat unik dan memiliki nilai ramah lingkungan dan dapat mengangkat pangan lokal serta memiliki rasa yang dapat diterima oleh lidah. Dengan adanya penelitian ini menunjukan bahwa minuman serbuk kulit mangga memiliki pasar yang potensial untuk dikembangkan lebih lanjut.
Effect of Sugar on Anthocyanins in Rosella Kombucha Drink
Authors: Lusiana Cici Sabarani  , Catur Retno Lestari  , Umar Hidayat
Published: September 2024

Rosella kombucha is a fermented drink with a high antioxidant content and is known to be a solution that can neutralize free radicals and prevent oxidative stress. The ingredients in rosella kombucha drinks are anthocyanins, flavonoids, vitamin C, and polyphenols. Anthocyanin is a natural color pigment produced by plants. Anthocyanin is an antioxidant that is beneficial for the body. The fermentation process for the rosella kombucha drink was carried out for 14 days. This research aimed to determine the effect of sugar concentration on anthocyanin levels in rosella (Hibiscus sabdariffa L.) kombucha drinks. The method used in this research is quantitative experimental with a completely randomized research design (RAL) with 3 samples and 3 repetitions. The independent variable in this study is sugar concentration, while the dependent variable is anthocyanin content. The subject of this research is sugar concentration, with concentrations of 15%, 25%, and 35%. Anthocyanin levels were measured using a visible spectrophotometer using a wavelength of 700 nm. Data analysis used in this research used the Shapiro-Wilk test and the One Way ANOVA test. The results of the data analysis show that the highest average is found in the F3 sample at 38.743900 ppm. The results of data analysis using the One Way ANOVA test showed a p-value <0.05, which means it has a real influence. The lowest pH value was found in sample F3, namely 2. The low pH helped stabilize the anthocyanin levels in the Rosella kombucha drink. The results showed that the sugar concentration used to make rosella kombucha drinks in all samples had a significant influence on anthocyanin levels (p<0.05).
USE OF BLOCKCHAIN FOR DATA PROTECTION AND TECHNOLOGY DRAWBACKS
Authors: Barakova Aliya , Ussatova Olga , Zhussipbekova Sholpan , Urazgalieva Sholpan , Shadinova Kunsulu
Published: September 2024

Blockchain technology offers significant opportunities for data protection, including decentralization, immutability, and security. However, its drawbacks, such as scalability issues and high energy consumption, require careful analysis. For effective use of blockchain in the field of data protection, it is essential to consider both its advantages and limitations. In this article, we examine the drawbacks of blockchain technology, with a particular focus on the scalability issue. We will analyze various approaches to solving this problem and thoroughly discuss the proposed solutions. Additionally, attention will be given to the concept of multidimensional blockchain and its structural features. In conclusion, we will present formulas and methods aimed at increasing transaction speed, which will help to better understand how multidimensional blockchains can serve as an effective solution for scalability.
ТЕХНИЧЕСКИЕ СРЕДСТВА ДИСТАНЦИОННОГО МОНИТОРИНГА С ПОМОЩЬЮ БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ ПЛАТФОРМ
Authors: Оксененко А.А. , Еримбетова А.С. , Куанаев А.А. , Мухамедиев Р.И. , Кучин Я.И.
Published: September 2024

Системы с использованием БПЛА стали популярными в последние десятилетия благодаря своей надежности в различныхкоммерческих сферах. Традиционные стационарные станции мониторинга качества воздуха имеют большие размеры и неподвижны, что ограничивает их функциональность. Эти недостатки в значительной степени устранены с помощью доступных сенсорных модулей, интегрированных с дронами. Оснащенные оптическими, мультиспектральными и гиперспектральными камерами, а также газовыми анализаторами, дроны обеспечивают получение детализированных данных без необходимости присутствия человека. Эти технологии находят применение в таких областях, как сельское хозяйство, здравоохранение и экология, позволяя быстро выявлять проблемы, контролировать процессы и предотвращать чрезвычайные ситуации. Настоящая работа представляет собой комплексный и систематизированный обзор коммерческого использования дронов для мониторинга качества воздуха и состояния окружающей среды. Собранная информация былаклассифицирована по моделям дронов, типам датчиков и весу полезной нагрузки, что критично для их стабильности. В большинстве исследований использовались недорогие датчики в зависимости от доступного пространства на дроне. Обзор существующих публикаций подчеркивает важность дальнейших исследований и разнообразие применения дронов дляэкологического мониторинга. Эта работа акцентирует значимость дронов как инструмента для повышения качества мониторинга, точности данных и управленческих решений. В условиях быстрого изменения современного мира важна дальнейшая разработка новых технологий для оптимизации дистанционного мониторинга, что может привести к более устойчивому управлению природными и городскими ресурсами. Эта работа окажет помощь исследователям и экологам в выборе подходящей полезной нагрузки для дронов и оптимальных мест их установки, а также в применении современныхметодов мониторинга, что будет полезно для разработки эффективных протоколов и баз данных для экологических целей.
DEVELOPMENT AND CREATION OF HYBRID EWT-LSTM-RELMIEWT MODELING IN HIGH-VOLTAGE ELECTRIC NETWORKS
Authors: Shermantayeva Zh. , Mamyrbayev O.
Published: September 2024

Modern energy is at the crossroads of cutting-edge technologies that are revolutionizing the way power systems are controlled, managed and optimized. Leading the way in this process are IoT (Internet of Things), FPGAs (programmable gate arrays), and microcontrollers, including powerful devices such as ESP32. These technologies not only significantly improve the efficiency and reliability of energy systems, but also open up new prospects for creating sustainable and intelligent energy infrastructures. In the Republic of Kazakhstan, energy systems are actively monitored and optimized in order to ensure stable development and meet the growing energy needs of society. The use of IoT technologies allows you to quickly collect data on the operation of energy networks, analyze electricity consumption and predict changes based on information from sensors installed in various network nodes. The use of an FPGA provides high-speed processing of large amounts of data, which is necessary for real-time monitoring and control in conditions of rapidly changing load and dynamic energy processes. These capabilities help improve system resiliency and prompt response to accidents or network anomalies that occur. In this article, we will look at how IoT technologies, FPGAs, and a hybrid approach to modeling using EWT-LSTM-RELM-IEWT, as well as ESP32 microcontrollers, affect the development of the energy industry in the Republic of Kazakhstan. We will analyze current and prospective solutions aimed at optimizing energy systems and ensuring sustainable development of the national energy sector in the face of modern challenges and requirements.
Sustainable and ecological construction with autoclaved aerated concrete
Authors: Walczak Paweł
Published: September 2024

The article is an introduction to the scope of legal requirements regarding CO2 emissions in the autoclaved aerated concrete industry. It addresses the issue of applicable regulations regarding ESG reporting, divided into individual scopes. The article presents the history of autoclaved aerated concrete as a universal and ecological material from the beginning of its development. It outlines the possibilities of achieving zero emission in the AAC industry, and the dependence of these goals on the country’s decarbonization plan. Additionally, it analyzes existing environmental product declarations [EPDs] of autoclaved aerated concrete and discusses possible investments that will reduce CO2 emissions throughout the product’s life cycle, including re-carbonation occurring during the use of autoclaved aerated concrete products.
Enhancing Seawater Reverse Osmosis Desalination Efficiency Using Digital Twins and Machine Learning
Authors: Daaboub Aissam , Echeverria Rovira Lluís , Rubion Soler Edgar
Published: September 2024

The future faces escalating water scarcity due to population growth, climate change, and inefficient resource management. Therefore, innovative solutions for sustainable access and usage are needed. Seawater Reverse Osmosis (SWRO) desalination stands out as a key technology in tackling this dilemma. However, SWRO is energy-intensive, primarily due to the need to pressurize seawater to overcome the osmotic pressure to produce fresh water. In this regard, real-time management of operating parameters in SWRO plants enables minimizing energy consumption and chemical usage and adjusting water production in response to demand and water conditions, highlighting the need for real-time monitoring and advanced simulation tools such as digital twins. In response, this study explores the potential of eleven machine learning algorithms to simulate the SWRO process using a vast dataset of 18.816 scenarios generated through a solution diffusion transport model. Our investigation covers both non-ensemble and ensemble models. Additionally, a Shapley additive explanation analysis was carried out to gain insights into the most influential predictors and confirm the model’s ability to comprehend the Reverse Osmosis (RO) process. The findings underscore the high accuracy of the algorithms, particularly XGBoost, CatBoost and ANN, in predicting key parameters such as permeate flow, permeate salinity and specific energy consumption. Furthermore, Support Vector Machine regression model shows promising in predicting permeate flow. These findings highlight the potential of data-driven models, particularly ensemble-based algorithms, in simulating SWRO behavior, laying the groundwork for future process optimization.
SynthRetina: Revolutionizing Fundus Image Analysis Through Synthetic Data Enhancement
Authors: Sharaf Najwa , Rashwan Hatem A. , Abdulwahab Saddam , Puig Domenec
Published: September 2024

In an era characterized by the rapid evolution of data-driven applications, the generation of high-quality synthetic data has become increasingly indispensable. This serves as a crucial element for advancing research, development, and ensuring the responsible management of sensitive information. However, the synthesis of fundus images presents unique challenges due to the intricate and highly detailed structures inherent in retinal images. While Generative Adversarial Networks (GANs) show promise in image synthesis, they often encounter training difficulties and struggle to produce truly realistic images. This paper introduces SynthRetina, an innovative system that harnesses the capabilities of GANs to generate lifelike fundus images. SynthRetina amalgamates a generator network and a discriminator network, facilitating the creation of synthetic fundus images with diverse applications across the medical field. The generator network specializes in transforming input fundus images from one class to another, while the discriminator network rigorously evaluates the authenticity of the generated images. SynthRetina effectively addresses the challenge of limited availability of medical data for research and development, offering a solution that enhances data augmentation and improves the performance of fundus image classification tasks. An evaluation of the SynthRetina architecture using a real fundus image dataset demonstrates its ability to produce a more diverse and realistic collection of fundus images compared to other GAN-based methods.
Predictive Maintenance in the Food Industry: A Case Study Using Vibration Sensors and Machine Learning Techniques
Authors: Fernández-Peláez Francisco , Etxegarai Maddi , Moreno Vanessa , Echeverria Lluís , Campins Gonzalo , López Carlos , Rojo Rubén , González Valentín
Published: September 2024

Predictive maintenance has emerged as a crucial strategy in industrial settings to enhance operational efficiency and minimize downtime. This study focused on implementing predictive maintenance in the context of the Monoblock machine, utilizing data from vibration sensors integrated with production stoppage records. The objective was to forecast future stoppages in the production line with a narrow time window to enable proactive maintenance interventions. Xtreme Gradient Boosting with oversampling techniques was employed to address the challenges of imbalanced data classes. Through extensive experimentation and analysis, promising results were achieved, demonstrating the ability to predict future stoppages with high accuracy within a 3-minute time window. The findings underscore the effectiveness of machine learning approaches in predictive maintenance applications, mainly when dealing with real-time sensor data and complex operational environments.
Detection of Epileptic Seizures with EEG Sequential Patterns
Authors: Montserrat Cristina , Fernandez Jonah , Innocenti Bianca , López Beatriz
Published: September 2024

The rise of wearable EEG devices has opened the opportunity to develop new tools for neurological disorder monitoring, particularly for conditions like epilepsy. Machine learning plays a key role in processing the EEG signal towards an assessment of the person’s state, and eventually evaluating some condition risk. However, existing approaches often rely on raw EEG data, keeping a numerical representation of the information contained in the data. Conversely, in a previous work, we explored representing the EEG signals using sequential patterns. In this work, we analyze the potential of such a representation through several machine learning methods, including decision trees, support vector machines, k-nearest neighbors, and random forest. The experiments carried out with the CHB-MIT scalp EEG database of Physionet show the outperformance of random forest.
Impact of Maternal Nutrition on Neonatal Birthweight: A Machine Learning Study
Authors: Mursil Muhammad , Rashwan Hatem A. , Santos-Calderon Luis , Murphy Michelle M. , Puig Domenec
Published: September 2024

Birthweight (BW) is a critical marker for neonatal health and subsequent development. Neonates born with low BW (LBW) face heightened risks of various health complications, both immediate and long-term. While previous research has predominantly delved into physiological, ultrasound, and lifestyle determinants of BW, recent clinical studies indicate a potential association between neonatal BW and maternal nutrition, including factors like vitamin B12, plasma folate, and iron levels. However, this area remains largely unexplored in terms of predictive analytics. This study aims to bridge this gap by exploiting various machine learning (ML) models to estimate BW and investigate the influence of maternal nutritional factors on BW. Leveraging a cohort of 729 pregnant women monitored during their second trimester in Reus and Tarragona, Spain, the extreme gradient boost model demonstrated robust predictive performance, achieving a mean absolute error (MAE) of 210 grams and an R-squared (R2) value of 0.857. The findings of this study not only validate the predictive significance of maternal nutritional status but also underscore plasma folate and Vitamin B12 as the most influential predictors of BW. Furthermore, the predictive capacity of the proposed model is clarified through partial dependence plots on how variations in maternal factors significantly affect BW.
Generative Models for Data Augmentation on Inertial Measurement Units Data Classification
Authors: Llauradó Blanca , Núñez José Fernando , Romero Axel , Arjona Jamie , Béjar Javier , Gómez-García Jorge A. , Torricelli Diego
Published: September 2024

Diagnosis using time series data is an important area in medical domains. Machine learning models rely on large collections of data for successful generalisation. However, data collection in fields such as medicine is difficult, which limits the effectiveness of these models. Although techniques like data augmentation can help increase dataset sizes, they work best mainly with image data and not as well with other types of data. This is where generative models can fill this gap. We present a study of the application of a range of generative models (TimeGAN, WaveGAN, DDPM) and data inpainting models (SSSD, ExtraMAE) for time series on the domain of the classification of Inertial Measurement Units (IMUs). The aim is to assess their capabilities and the improvements obtained when used for data augmentation with different training and transfer learning methods. The results show that these methods generate synthetic data that, when added to the training data or used as pretraining data, improve accuracy. GAN methods lag behind diffusion denoising methods in generating realistic data, and are also more difficult to train. Inpainting methods obtained results similar to GAN methods but generated samples more similar to the real data and with a more stable training.
Fuzzy Logic-Based Variable Encoding for Improved Diabetic Retinopathy Prediction
Authors: Pascual-Fontanilles Jordi , Valls Aida , Moreno Antonio , Romero-Aroca Pedro
Published: September 2024

Electronic Health Records (EHRs) contain valuable historical information for building clinical decision support systems. In this study, we focus on exploring novel techniques for improving the prediction of the severity degree of Diabetic Retinopathy (DR) in Diabetes Mellitus patients. In a previous paper, we evaluated the behaviour of different classifiers using the patients’ retrospective EHR data to assess their current level of DR, achieving good results. Continuing that work, we now focus on studying different methods for encoding numerical variables, in order to improve the accuracy of these predictions. We propose three normalization methods based on fuzzy sets for encoding numerical data. Because of the inherent uncertainty of medical data, using fuzzy logic to represent the numerical variables can enhance the accuracy of a classifier. The results of the experimental tests, conducted on a dataset of 2108 patients, show that for low-complexity classifiers (such as KNN or CNN) a classical fuzzification technique works the best, while for more complex architectures (like TapNet or ResNet) a fuzzy two-hot encoding gives the best performance. The final aim of the research is to build a clinical decision support system that can make an accurate and personalised prediction of DR evolution.
Unsupervised Deep Learning Architectures for Anomaly Detection in Brain MRI Scans
Authors: Malé Jordi , Xirau Víctor , Fortea Juan , Heuzé Yann , Martínez-Abadías Neus , Sevillano Xavier
Published: September 2024

Brain imaging techniques, particularly magnetic resonance imaging (MRI), play a crucial role in understanding the neurocognitive phenotype and associated challenges of many neurological disorders, providing detailed insights into the structural alterations in the brain. Despite advancements, the links between cognitive performance and brain anatomy remain unclear. The complexity of analyzing brain MRI scans requires expertise and time, prompting the exploration of artificial intelligence for automated assistance. In this context, unsupervised deep learning techniques, particularly Transformers and Autoencoders, offer a solution by learning the distribution of healthy brain anatomy and detecting alterations in unseen scans. In this work, we evaluate several unsupervised models to reconstruct healthy brain scans and detect synthetic anomalies.
Open Source Cardiac Digital Twinning of Human Ventricular Repolarisation from 12-Lead ECG and MRI
Authors: Camps Julia , Wang Zhinuo Jenny , Doste Ruben , Berg Lucas Arantes , Holmes Maxx , Lawson Brodie , Tomek Jakub , Burrage Kevin , Bueno-Orovio Alfonso , Rodriguez Blanca
Published: September 2024

Cardiac digital twins represent the required functional mechanisms of patient hearts to evaluate therapies and inform clinical decision-making virtually. A scalable generation of cardiac digital twins can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source digital twinning framework for personalising electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG). We extended a Bayesian-based inference framework to infer electrical repolarisation characteristics. Fast simulations are conducted with a decoupled reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics. Parameter uncertainty is represented by inferring a population of ventricular models rather than a single one, which means that parameter uncertainty can be propagated to virtual therapy evaluations. The framework is demonstrated in a healthy female subject, where our inferred reaction-Eikonal models reproduced the patient’s ECG with a Pearson’s correlation coefficient of 0.93. The methodologies for cardiac digital twinning presented here are a step towards personalised virtual therapy testing. The tools developed for this study are open-source, ensuring accessibility, inclusivity, and reproducibility, this is available on GitHub.
Speech Synthesis of Valencian Using a Conditional Variational Autoencoder with Adversarial Learning
Authors: Aragó Joan , Freixes Marc
Published: September 2024

The growing demand for high-quality speech synthesis systems in minority languages presents a notable challenge for researchers. In response, this study focuses on synthesizing Valencian speech to develop an effective text-to-speech system for this linguistic variety. A meticulously recorded corpus, comprising 7 hours of speech data, was utilised to train a model based on a conditional variational autoencoder with adversarial learning, specifically Variational Inference with adversarial learning for end-to-end Text-to-Speech (VITS). Additionally, a pretrained multispeaker model was fine-tuned using 30 minutes, and the entire corpus. Perceptual testing was conducted to evaluate the synthesised speech quality, revealing promising results. Notably, the proposed model demonstrated competitiveness compared to the recently released Valencian model by the Aina project, indicating its efficacy in generating natural and fluent Valencian speech. These findings contribute to advancing the field of Valencian text-to-speech synthesis and carry implications for the development of speech synthesis systems in other minority languages.
Decision Support System for the Diagnosis of Chronic Wounds Using Artificial Intelligence Algorithms on Images
Authors: Casanova Lorena , Reifs David , Reig Ramon , Grau Sergi
Published: September 2024

A solution is proposed that consists of supporting the professional in deciding how to act on the wound by offering a diagnosis proposal. Artificial Intelligence (AI) algorithms have been developed to allow the extraction of the most relevant wound characteristics through an image and providing similar successful wounds from the health center itself. Five pre-trained Convolutional Neural Networks (CNN) have been used to compare the results with images processed in different ways. In this way, the professional would have a diagnostic reference of other wounds similar to the one being evaluated and thus be able to make the right decision. A total of 711 images were processed and analyzed in order to obtain their most identifying morphological and textural characteristics. From each of the images, the five most similar images in terms of characteristics were searched for and clinically validated by comparing them using an objective assessment scale. The results showed an overall accuracy of 71.12%, calculated as the weighting of the scale match of similar images to the original. With this solution, clinicians improve their confidence in clinical practice by having support in decision making, observing favorable outcomes and progression of chronic wounds.
From Clustering to Intelligent Decision Support System: An Application to 3D Printing
Authors: Karna Ashutosh , Gibert Karina
Published: September 2024

This study focuses on developing an intelligent decision support system (IDSS) that helps a human operator make data-driven decisions. To put IDSS in production, it is necessary to develop two additional components: one oriented to recognize the cluster of new data and the other a knowledge-based resulting from the interpretation of clusters and further association of actions to each cluster, constituting a knowledge base with the alerts and recommendations associated to every profile. Bootstrap-CURE technique is used to handle the initial component, whereas a meta-clustering framework is suggested for interpreting the clusters and providing recommendations. A detailed strategy is presented for handling a print job, examining patterns, and executing actions through IDSS, thus improving predictive accuracy and operational efficiency. Two distinct machine learning models were developed, one to detect the operational mode and another to choose the best meta-cluster for the type of printing jobs and detained steps are provided for implementing the recommendations.
The DeuteroNoise Dataset: An Initial Exploration of an Underwater Vessel Sound Dataset Within the Framework of a JPI-Oceans Project
Authors: Nou-Plana Ignasi , Freixes Marc , Vaquerizo-Serrano Jesús , Arnela Marc , Cañestro Cristian , Quintana Eva R. , Teaca Adrian , Chatzigeorgiou Marios , Ristoratore Filomena , Zambon Giovanni , Manni Lucia , Alsina-Pagès Rosa Ma.
Published: September 2024

The vast and largely unexplored underwater environment is a rich source of diverse sound events. These sounds, which range from the calls of marine life to the noise generated by human activities, create a complex acoustic environment. Research has been conducted on gathering and categorizing this type of data. However, only a few databases have been recently openly published focusing on anthropogenic sounds. This paper outlines the preliminary steps towards the creation of a comprehensive dataset for the detection of underwater sound events, with an initial emphasis on the sounds of vessels and boats. Within the framework of the JPI Oceans project, DeuteroNoise, a wide spectrum of vessel sounds under varying conditions has been captured and annotated with the necessary metadata for sound event detection tasks. Moreover, the proposed dataset will facilitate the development of more accurate and adaptable vessel sound event detection models and encourage further research in this area. The dataset contains raw audio files and the respective initial analysis based on labeled events, duration of the events, signal-to-noise ratio (SNR) and impact measurements. The retrieved data can be grouped into noisy and non-noisy spots. The noisy locations include the Port of Barcelona (Spain), the Port of Constant,a (Romania), and the Lagoon of Venice (Italy). In contrast, non-noisy data was collected during two measurement campaigns at Pont del Petroli in Badalona (Spain). In addition to the dataset, to illustrate its potential application, a classifier for vessel/boat sound events is proposed. The classifier uses mel spectrograms as input data and is built on a pre-trained model that leverages a residual neural network. This system is capable of classifying vessel/boat related events from background sound environment.
A Multi-Armed Bandit Approach Using Implicit Hitting Sets for Weighted CSPs
Authors: Petrova Aleksandra , Larrosa Javier , Rollon Emma
Published: September 2024

This ongoing work outlines a Multi-Armed Bandit (MAB) algorithm using the Implicit Hitting Set approach (IHS) to solve Weighted Constraint Satisfaction Problems (WCSP). The paper presents the idea of using MAB to choose among different versions of IHS and efficiently solve the WCSP instance. For the purpose of this work, we create an agent that employs the Roulette Wheel strategy. The agent is then tested on a set of random instances. We show that the MAB does not reach the performance of the best IHS alternative, but consistently outperforms the average, which indicates that our approach is promising.
A Tool for Reading Evaluation in Arithmetic Word Problems
Authors: Álvarez-García Elena , García-Costa Daniel , Paniagua Sandra , Sanz Maria T. , Santagueda Maria , Mocholí Pablo , López-Iñesta Emilia , Grimaldo Francisco
Published: September 2024

AWPSolver is a tool created to analyse reading in Arithmetic Word Problems (AWPs). The main objective of this work is the development of an easy-to-use and effective tool to measure the relation between reading speed and probability of success of AWPs. The tool allows the creation of experiments based on problem sets and the recording of reading and solving times, as well as the number of correct answers. The results obtained after analysing the data collected from a sample of 276 students highlight the differences in reading and comprehension of the problems between school years and genders. Through AWPSolver we propose an efficient way to measure, examine and monitor the reading process in a mathematical context for school students.
Assessment of the Nutritional Status of Adolescents in Orphanage from Lahore, Pakistan
Authors: Khan Hamala , Sohail Zoha , Khan Noohela , Zahra Shazia , Niazii Rameen Khan , Anwaar Usama , Zaheer Ayesha , Moazzam Mnahil , Arshad Dua
Published: September 2024

Orphans are one of the most vulnerable groups to nutritional deficiency, which require more nutritional care for proper growth and development. Assessment of nutritional status has cornerstone importance in orphans because growth retardation is mainly caused by undernutrition. As United Nations indicated in a recent report, there are more than 4.3 million orphan children in Pakistan and many of them are under age nearly of 17. The study aims to assess adolescent’s nutritional status and the relationship between BMI and their dietary habits. The current study is a cross-sectional study conducted on 80 orphan children aged (10-19 years). The sample of orphan children was taken from three orphanages in Lahore via non-probability convenient sampling. The data was collected using a questionnaire that included questions related to anthropometric measurements, clinical assessment, and food frequency tables. Data were analyzed by using SPSS software version 25. The frequency of orphans with normal BMI was 51.2% among the study sample. Indicatively, majority of children were suffering from paleness and dryness of skin, white spots in nails, redness in eyes, dry and brittle hair, and discolored or spongy gums. Dietary history showed that consumption of bread and cereals, starchy vegetables, junk food, and carbonated beverages was more common among orphanages. Orphan children in diverse orphanages had sub-clinical nutritional deficiencies due to poor dietary habits; emphasizing the need for detailed assessment of nutritional status in orphanages for the applications of related interventions to prevent nutritional deficiencies.