Involvement in Parasocial Relationships among Middle-Aged Women by A.M. Molokostova, M.N. Kosmacheva

“…Ferguson [11]. Statistical data processing was conducted using the SPSS program. …”
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Psychometric validation of the Thai version of the 12-item Zarit Burden Interview among informal caregivers in a palliative care setting in the south of Thailand. by Ueamporn Summart, Thanaporn Kaewbubpha, Ornicha Saengnil, Prasertsri Kongkaur, Suparuek Mahorasop, Suchittra Saengkhum, Yuwadee Wittayapun

“…A methodological study examining the validity of the 12-item ZBI was carried out with informal caregivers of palliative care patients of two district health service networks. The data were gathered using self-administered questionaries, and then all participants were randomly split into two sub-samples for exploratory factor analysis (EFA) (n = 150) and confirmatory factor analysis (CFA) (n = 155). …”
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Optimization and Machine Learning in Modeling Approaches to Hybrid Energy Balance to Improve Ports’ Efficiency by Helena M. Ramos, João S. T. Coelho, Eyup Bekci, Toni X. Adrover, Oscar E. Coronado-Hernández, Modesto Perez-Sanchez, Kemal Koca, Aonghus McNabola, R. Espina-Valdés

“…A neural network time series (NNTS) model trained on average year data achieved high accuracy (R²: 0.9253–0.9695). …”
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Efficient Ensemble Learning-Based Models for Plastic Hinge Length Prediction of Reinforced Concrete Shear Walls by Naser Safaeian Hamzehkolaei, Mohammad Sadegh Barkhordari

“…Deep neural network-based ensemble learning models including Simple Averaging Ensemble (SAE), Stacking Ensemble (SE), Snapshot Ensemble (SSE), and Deep Forest (DP), were leveraged. …”
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Studying and Comparing the Soil Characteristics of Gullies (Case Study: Chabar and Sistan Region) by Mansour Jahantigh, Marzieh Tabe

“…So, it turns out that the soil characteristic and soil erodible make sub network in study gullies.…”
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Energy management system for PV-based distributed generators in AC microgrids using an adapted JAYA optimizer to minimize operational costs, energy losses, and CO2 emissions by Nicolás Ignacio Labra-Cáceres, Luis Fernando Grisales-Noreña, Rubén Iván Bolaños, Jhony Andrés Guzmán-Henao, Oscar Danilo Montoya

“…To solve the model, an adapted version of the JAYA optimization algorithm was implemented, and its performance was compared against four established methodologies: the Chu & Beasley Genetic Algorithm (CBGA), Particle Swarm Optimization (PSO), the Vortex Search Algorithm (VSA), and Ant Lion Optimization (ALO). The analysis was conducted using two test microgrids: one with 33 nodes operating in grid-connected mode and another with 27 nodes operating in islanded mode, utilizing generation and demand data from Medellín and Capurganá, Colombia. …”
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Wavelet Decomposition-Based AVOA-DELM Model for Prediction of Monthly Runoff Time Series and Its Applications by ZHANG Yajie

“…For the improvement in prediction accuracy of monthly runoff time series,a prediction model is proposed,which combines the wavelet decomposition (WD),African vultures optimization algorithm (AVOA),and deep extreme learning machine (DELM),and it is applied to the monthly runoff prediction of Yale Hydrological Station in Yunnan Province.Specifically,WD decomposes the time-series data of monthly runoff to obtain highly regular subsequence components,and AVOA is employed to optimize the number of neurons in the hidden layers of DELM;then,the WD-AVOA-DELM model is built to predict each subsequence component,and the prediction results are summated and reconstructed to produce the final prediction results of monthly runoff.Meanwhile,models based on the support vector machine (SVM) and BP neural networks are constructed for comparative analysis,including WD-AVOA-SVM,WD-AVOA-BP,AVOA-DELM,AVOA-SVM,and AVOA-BP models.The results reveal that the average absolute percentage error of the WD-AVOA-DELM model for the monthly runoff prediction of Yale Hydrological Station is 3.02%;the prediction error is far less than that of WD-STOA-SVM and WD-AVOA-BP models,and the prediction accuracy is more than one order of magnitude higher than that of AVOA-SVM,AVOA-SVM,and AVOA-BP models.The result indicates that the proposed model has good prediction performance.In this model,WD can scientifically reduce the complexity of runoff series and raise the prediction accuracy;AVOA can effectively optimize the key parameters of DELM and improve the performance of DELM networks.…”
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A Wearable Sensor Node for Measuring Air Quality Through Citizen Science Approach: Insights from the SOCIO-BEE Project by Nicole Morresi, Maite Puerta-Beldarrain, Diego López-de-Ipiña, Alex Barco, Oihane Gómez-Carmona, Carlos López-Gomollon, Diego Casado-Mansilla, Maria Kotzagianni, Sara Casaccia, Sergi Udina, Gian Marco Revel

“…Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. …”
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Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory by Sergei Manzhos, Johann Lüder, Pavlo Golub, Manabu Ihara

“…A hybrid Gaussian process regression—neural network method was used to understand the type of functional dependence of $\overline\tau$ on the features which contained cell-averaged terms of the 4th order gradient expansion and the product of the electron density and Kohn–Sham (KS) effective potential. …”
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Facteurs associés aux avortements provoqués à l’hôpital de référence du district sanitaire de Sotouboua, Togo, 2020 à 2023 by Lawouratou Okotan, Péléké Mawaba Hilim, Rebecca Kinde, Ablaga Amenyido, Malilioubè Tchandana

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Modeling of Battery Storage of Photovoltaic Power Plants Using Machine Learning Methods by Rad Stanev, Tanyo Tanev, Venizelos Efthymiou, Chrysanthos Charalambous

“…The results show that the heterogeneous ensemble model with three decision trees and linear regression as main model achieves the highest average R<sup data-eusoft-scrollable-element="1">2</sup> of 0.792 and the second-best nRMSE of 0.669% using a 30-day training period. …”
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Monitoring the production, transfer and promotion of evidence-based knowledge in research centers of Ilam University of Medical Sciences and providing solutions for improvement: A... by Masomeh Mirzaee, Peigham Heidarpoor, Jafar Bazyar

“…Descriptive statistics was used for quantitative data and content analysis was used for the qualitative information. …”
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A hybrid LSTM-transformer model for accurate by Tianren Zhao, Yanhui Zhang, Minghao Wang, Wei Feng, Shengxian Cao, Gong Wang

“…Firstly, time-series samples are constructed from raw battery data, and physically consistent temperature-derived features—including average temperature, temperature range, and temperature fluctuation—are engineered. …”
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FTIR-based molecular fingerprinting for the rapid classification of dengue and chikungunya from human sera using machine learning: an observational studyResearch in context by Souvik Das, Subhanita Roy, Aritri Bir, Arindam Ghosh, Tarun Kanti Bhattacharyya, Pooja Lahiri, Basudev Lahiri

“…Vibrational spectra were acquired using FTIR and Raman techniques, followed by spectral deconvolution and machine learning-based classification using Support Vector Machine (SVM), Neural Network (NN), and Random Forest (RF) models. Findings: FTIR analysis revealed distinctive group-specific vibrational signatures, particularly in the Amide I and III regions, where dengue-infected sera exhibited a marked increase in β-sheet content and loss of α-helical structures. …”
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Examining the social distributions in neighbourhood black carbon and ultrafine particles in Montreal and Toronto, Canada by Emmanuelle Batisse, Marshall Lloyd, Alicia Cavanaugh, Arman Ganji, Junshi Xu, Marianne Hatzopoulou, Jill Baumgartner, Scott Weichenthal

“…Areas were classified as disadvantaged, intermediate, or advantaged following K-means clustering analysis. We aggregated and calculated population-weighted average concentrations of BC and UFP, and UFP size at the dissemination area and cluster levels using high-resolution exposure surfaces, derived from year-long mobile monitoring campaigns conducted in each city during 2020–2021. …”
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Initial Study of Column-Integrated Aerosol Optical Properties over Birkat al Mouz, Sultanate of Oman by Baiju Dayanandan, Nirmal Krishna, Ajay Parottil, Vinoj Velu, Ahmed Al Harrasi, Binisia Sanatan, Pawan Gupta, Tom Eck, Alexander Smirnov, Humaid AlBadi

“…Abstract Purpose This study aims to provide the first analysis of aerosol optical properties, radiative forcing, and source identification over Birkat al Mouz, Oman, using Aerosol Robotic Network (AERONET) data from December 2022 to November 2024. …”
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Landscapes of Trade by Merten Nefs

“…The Dutch logistics complex since 1980 offers a critical European case for the analysis, due to the clear shifts in its spatial pattern and planning system, as well as a fierce and well-documented planning discourse featuring various information sources and actor networks. …”
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Deep Learning-Based Postural Asymmetry Detection Through Pressure Mat by Iker Azurmendi, Manuel Gonzalez, Gustavo García, Ekaitz Zulueta, Elena Martín

“…In this paper we will discuss the application of deep learning algorithms in the analysis of pressure data for the detection of postural asymmetries in 139 patients aged 3 to 20 years. …”
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Research Collaboration Dynamics at the National Nuclear Energy Commission (CNEN): Pathways to Enhance Economic and Social Impact by ALESSANDRO A. JORDÃO, Valter Arthur

“…Based on a descriptive and evaluative informetric analysis of internationally indexed publications in the Scopus (Elsevier) database, accessed through the SciVal platform, it was found that publications co-authored by CNEN with industry, government, and international actors, although less frequent, achieved greater resonance and influence in international knowledge networks, attracted more attention and interest from the international community, significantly exceeding the average expectation for similar publications, and exerted a more significant influence on patentable technologies and public policy formulation. …”
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BCI‐control and monitoring system for smart home automation using wavelet classifiers by Amer Al‐Canaan, Hicham Chakib, Muhammad Uzair, Shuja‐uRehman Toor, Amer Al‐Khatib, Majid Sultan

“…The deep learning and LDA classifiers models produced the best performance with average accuracy of 95.6% and 96% for both training and testing data sets.…”
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