bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial intelligence strategies for simulating the integrated energy systems
Authors: Talaat M. , Tayseer M. , Farahat M. A. , Song Dongran 
Published: April 2024

AbstractIn recent decades, the operational impact of Artificial Intelligence (AI) strategies is massively dominating the scientific arena of improving the operation of energy systems and their hybrid integrations. Comprehensively, this paper highlights the firm methodological link of AI strategies with the different defined categories of numerical methods in hypothetically simulating the complex integrated energy systems especially the integration of Renewable Energy Sources (RES). The conducted studies in this paper are related to the bifurcations of the applied numerical simulation methodologies for efficient energy systems and the practical implementations of the optimal operated energy systems considering the integration scenarios of these methodologies with AI strategies. Furthermore, this research reviews innovatively several case studies and practical examples to emphasize the effective contributions of AI strategies in enhancing the computational analysis of numerical simulation methods forming a smart approach for assessing experimental studies that are associated with energy systems. Finally, this paper deeply discusses the concept of integration either in the hybrid controlling strategies combining AI with numerical simulation methods or in combining different energy systems in one hybrid model for reliable operation considering the complexity level.
Video-based beat-by-beat blood pressure monitoring via transfer deep-learning
Authors: Omer Osama A. , Salah Mostafa , Hassan Loay , Abdelreheem Ahmed , Hassan Ammar M. 
Published: April 2024

Abstract
Currently, learning physiological vital signs such as blood pressure (BP), hemoglobin levels, and oxygen saturation, from Photoplethysmography (PPG) signal, is receiving more attention. Despite successive progress that has been made so far, continuously revealing new aspects characterizes that field as a rich research topic. It includes a diverse number of critical points represented in signal denoising, data cleaning, employed features, feature format, feature selection, feature domain, model structure, problem formulation (regression or classification), and model combinations. It is worth noting that extensive research efforts are devoted to utilizing different variants of machine learning and deep learning models while transfer learning is not fully explored yet. So, in this paper, we are introducing a per-beat rPPG-to-BP mapping scheme based on transfer learning. An interesting representation of a 1-D PPG signal as a 2-D image is proposed for enabling powerful off-the-shelf image-based models through transfer learning. It resolves limitations about training data size due to strict data cleaning. Also, it enhances model generalization by exploiting underlying excellent feature extraction. Moreover, non-uniform data distribution (data skewness) is partially resolved by introducing logarithmic transformation. Furthermore, double cleaning is applied for training contact PPG data and testing rPPG beats as well. The quality of the segmented beats is tested by checking some of the related quality metrics. Hence, the prediction reliability is enhanced by excluding deformed beats. Varying rPPG quality is relaxed by selecting beats during intervals of the highest signal strength. Based on the experimental results, the proposed system outperforms the state-of-the-art systems in the sense of mean absolute error (MAE) and standard deviation (STD). STD for the test data is decreased to 5.4782 and 3.8539 for SBP and DBP, respectively. Also, MAE decreased to 2.3453 and 1.6854 for SBP and DBP, respectively. Moreover, the results for BP estimation from real video reveal that the STD reaches 8.027882 and 6.013052 for SBP and DBP, respectively. Also, MAE for the estimated BP from real videos reaches 7.052803 and 5.616028 for SBP and DBP, respectively.

Graphical abstract
Proposed camera-based blood pressure monitoring system
RiskSchools: a prioritization-based system for the risk assessment of school buildings combining rapid visual screening smartphone app and detailed vulnerability analysis
Authors: Karafagka Stella , Riga Evi , Oikonomou George , Karatzetzou Anna , Fotopoulou Stavroula , Pitilakis Dimitris , Pitilakis Kyriazis 
Published: April 2024

AbstractA multi-purpose and multi-scale tool for the seismic vulnerability and risk classification of critical buildings, such as schools, is proposed for pre- and post-event decision-making to mitigate the risk and reduce losses. The herein proposed “RiskSchools” system, is capable of performing the seismic risk assessment and grading of school buildings at various scales (district, municipality, region etc.), using (a) a pre-seismic rapid visual screening and grading of the school buildings in different vulnerability-risk classes and (b) a seismic risk assessment of the school buildings population, applying probabilistic or scenario-based methods for the seismic hazard and analytical methods for the vulnerability and risk assessment, also leading to a grading of the buildings’ risk. The results of the two approaches are compared and combined through a flexible and adaptable expert elicitation scheme to provide a final classification of the seismic risk of the school buildings in the scale of interest and a prioritization scheme with respect to the need for seismic upgrade and retrofitting. The RiskSchools system consists of a powerful, state-of-the-art, user-friendly, and easy-to-use smartphone application for the compilation of the inventory and the rapid visual screening, and a project-dedicated multi-purpose webGIS platform for the seismic vulnerability and risk classification of school buildings at any scale. Although it is initially developed and applied to the school building stock of the Region of Central Macedonia in Greece, it has been specifically designed to be easily applied to other regions of Greece and worldwide and adapted to other critical buildings, like health care and hospital buildings. The ultimate scope of the RiskSchools System is to allow for the optimal design of decision-making procedures in support of disaster management to enhance critical buildings resilience.
Long-term effects of aromatase inhibitor withdrawal on bone mineral density in early breast cancer patients: 10-year follow-up results of the BREX study
Authors: Blomqvist Carl , Vehmanen Leena , Kellokumpu-Lehtinen Pirkko-Liisa , Huovinen Riikka , Ruohola Johanna , Penttinen Heidi , Sievänen Harri , Nikander Riku , Utriainen Meri , Saarto Tiina 
Published: April 2024

Abstract
Purpose
We aimed to provide long-term bone mineral density (BMD) data on early breast cancer patients of the BREX (Breast Cancer and Exercise) study. The effects of exercise and adjuvant endocrine treatment 10 years after randomization were analyzed, with special emphasis on aromatase inhibitor (AI) therapy discontinuation at 5 years.

Methods
The BREX study randomized 573 pre- and postmenopausal breast cancer patients into a 1-year supervised exercise program or a control group. 372 patients were included into the current follow-up analysis. BMD (g/cm2) was measured by dual-energy X-ray absorptiometry at lumbar spine (LS), left femoral neck (FN), and the total hip. Separate groups were displayed according to baseline menopausal status, and whether the patient had discontinued AI therapy at 5 years or not.

Results
The BMD change from 5 to 10 years did not significantly differ between the two randomized arms. AI discontinuation at 5 years had statistically significant BMD effects. The FN BMD continued to decrease in patients who discontinued AI therapy during the first 5-year off-treatment, but the decrease was three-fold less than in patients without AI withdrawal (− 1.4% v. − 3.8%). The LS BMD increased (+ 2.6%) in patients with AI withdrawal during the first 5 years following treatment discontinuation, while a BMD decrease (-1.3%) was seen in patients without AI withdrawal.

Conclusion
This study is to our knowledge the first to quantify the long-term impact of AI withdrawal on BMD. Bone loss associated with AI therapy seems partially reversible after stopping treatment. Trial registration: http://www.clinicaltrials.gov/ (Identifier Number NCT00639210).
Relevance of Thymic Stromal Lymphopoietin on the Pathogenesis of Glioblastoma: Role of the Neutrophil
Authors: Infante Cruz Alejandra , Coronel Juan Valentin , Saibene Vélez Paula , Remes Lenicov Federico , Iturrizaga Juan , Abelleyro Martín , Rosato Micaela , Shiromizu Carolina Maiumi , Candolfi Marianela , Vermeulen Mónica , Jancic Carolina , Yasuda Ezequiel , Berner Silvia , Villaverde Marcela Solange , Salamone Gabriela Verónica 
Published: April 2024

AbstractGlioblastoma multiforme (GBM) is the most predominant and malignant primary brain tumor in adults. Thymic stromal lymphopoietin (TSLP), a cytokine primarily generated by activated epithelial cells, has recently garnered attention in cancer research. This study was aimed to elucidate the significance of TSLP in GBM cells and its interplay with the immune system, particularly focused on granulocyte neutrophils. Our results demonstrate that the tumor produces TSLP when stimulated with epidermal growth factor (EGF) in both the U251 cell line and the GBM biopsy (GBM-b). The relevance of the TSLP function was evaluated using a 3D spheroid model. Spheroids exhibited increased diameter, volume, and proliferation. In addition, TSLP promoted the generation of satellites surrounding the main spheroids and inhibited apoptosis in U251 treated with temozolomide (TMZ). Additionally, the co-culture of polymorphonuclear (PMN) cells from healthy donors with the U251 cell line in the presence of TSLP showed a reduction in apoptosis and an increase in IL-8 production. TSLP directly inhibited apoptosis in PMN from GBM patients (PMN-p). Interestingly, the vascular endothelial growth factor (VEGF) production was elevated in PMN-p compared with PMN from healthy donors. Under these conditions, TSLP also increased VEGF production, in PMN from healthy donors. Moreover, TSLP upregulated programed death-ligand 1 (PDL-1) expression in PMN cultured with U251. On the other hand, according to our results, the analysis of RNA-seq datasets from Illumina HiSeq 2000 sequencing platform performed with TIMER2.0 webserver demonstrated that the combination of TSLP with neutrophils decreases the survival of the patient. In conclusion, our results position TSLP as a possible new growth factor in GBM and indicate its modulation of the tumor microenvironment, particularly through its interaction with PMN.
Graphical Abstract

Protumoral activity of TSLP. Neutrophils (derived from GBM patients) and GBM cells (under EGF stimulus) not only produce TSLP but also express its receptor. TSLP induces PDL1 expression and decreases apoptosis on both GBM cells and neutrophils. TSLP also increases proliferation and satellite development on GBM cells, whereas favors more neutrophil infiltration by increasing IL8 production.
Cost and response time optimization of edge architectures
Authors: Entrialgo Joaquín , Moreno-Vozmediano Rafael , Díaz José Luis 
Published: April 2024

AbstractThe cloud service provider market has recently expanded its offerings by providing edge as a service. This involves offering resources equivalent to those already available in the cloud, but through data centers located closer to the end user, with the goal of improving service latencies. Application providers face the challenge of selecting appropriate resources, both from the edge and cloud, to deploy their applications in a way that minimizes deployment costs while satisfying latency requirements. This paper presents Edarop (EDge ARchitecture OPtimization), an innovative orchestration mechanism for the optimal allocation of virtual machines in geographically distributed edge and cloud infrastructures. Edarop is capable of handling different edge and cloud vendors, each offering various types of VMs in different regions, with different prices, and network latencies. It also supports multiple simultaneous applications with different latency requirements and load profiles. Edarop employs Integer Linear Programming (ILP) to ensure the globally optimal solution within a reasonable time frame for the considered use cases. Several variants of the mechanism are provided, depending on whether the objective is to minimize cost, response times, or both. These variants are compared to each other and to alternative approaches, with the results showing that, unlike other methods, Edarop consistently respects latency constraints while minimizing the proposed objectives.
An enhanced sea-horse optimizer for solving global problems and cluster head selection in wireless sensor networks
Authors: Houssein Essam H. , Saad Mohammed R. , Çelik Emre , Hu Gang , Ali Abdelmgeid A. , Shaban Hassan 
Published: April 2024

AbstractAn efficient variant of the recent sea horse optimizer (SHO) called SHO-OBL is presented, which incorporates the opposition-based learning (OBL) approach into the predation behavior of SHO and uses the greedy selection (GS) technique at the end of each optimization cycle. This enhancement was created to avoid being trapped by local optima and to improve the quality and variety of solutions obtained. However, the SHO can occasionally be vulnerable to stagnation in local optima, which is a problem of concern given the low diversity of sea horses. In this paper, an SHO-OBL is suggested for the tackling of genuine and global optimization systems. To investigate the validity of the suggested SHO-OBL, it is compared with nine robust optimizers, including differential evolution (DE), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), fitness dependent optimizer (FDO), Harris hawks optimization (HHO), chimp optimization algorithm (ChOA), Fox optimizer (FOX), and the basic SHO in ten unconstrained test routines belonging to the IEEE congress on evolutionary computation 2020 (CEC’20). Furthermore, three different design engineering issues, including the welded beam, the tension/compression spring, and the pressure vessel, are solved using the proposed SHO-OBL to test its applicability. In addition, one of the most successful approaches to data transmission in a wireless sensor network that uses little energy is clustering. In this paper, SHO-OBL is suggested to assist in the process of choosing the optimal power-aware cluster heads based on a predefined objective function that takes into account the residual power of the node, as well as the sum of the powers of surrounding nodes. Similarly, the performance of SHO-OBL is compared to that of its competitors. Thorough simulations demonstrate that the suggested SHO-OBL algorithm outperforms in terms of residual power, network lifespan, and extended stability duration.
Time series clustering with random convolutional kernels
Authors: Jorge Marco-Blanco , Rubén Cuevas 
Published: April 2024

AbstractTime series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis methods, however, often struggle with these complexities. To address these limitations, we introduce R-Clustering, a novel method that utilizes convolutional architectures with randomly selected parameters. Through extensive evaluations, R-Clustering demonstrates superior performance over existing methods in terms of clustering accuracy, computational efficiency and scalability. Empirical results obtained using the UCR archive demonstrate the effectiveness of our approach across diverse time series datasets. The findings highlight the significance of R-Clustering in various domains and applications, contributing to the advancement of time series data mining.
Teacher Motivational Scaffolding and Preschoolers’ Motivational Triggers in the Context of Playful Learning of Multiliteracy and Digital Skills
Authors: Ollonen Briitta , Kangas Marjaana 
Published: April 2024

AbstractThis research focuses on exploring teacher motivational scaffolding and preschoolers motivational triggers in a playful learning project conducted in a Finnish preschool context. The aim of playful learning was to promote preschoolers’ multiliteracy and digital skills in a news-making project. The participants were 17 preschoolers and their teacher. Video ethnography and qualitative content analysis were employed as research methods. The findings indicate that the teacher’s motivational scaffolding strategies in playful learning pedagogy included connecting with the preschoolers’ prior experiences and interests, promoting exploration skills, supporting the different developmental levels of the preschoolers, and encouraging them to participate in playful learning. The following motivational triggers were identified for preschoolers: 1) engaging in discussions about news, 2) exploring news through traditional and digital media, 3) utilizing digital tools, and 4) creating and engaging in news-related activities. The findings contributed to the development of a motivational design model for playful learning.
Carbon footprint of higher education institutions
Authors: Paredes-Canencio Kevin Nabor , Lasso Ana , Castrillon Rosaura , Vidal-Medina Juan R. , Quispe Enrique C. 
Published: April 2024

AbstractHigher education institutions (HEIs) play a fundamental role in sustainability, promoters of innovation, science, and technology. Therefore, every day more institutions are joining the fight against global warming. One of the contributions of HEIs is the carbon footprint (CF) report, to implement policies and management systems to establish strategies to reduce polluting gas emissions from their campuses. In the present study, a systematic review was carried out for 50 reports of CF, where 94% of the studies were published from 2018 to 2022. This research compiles methodologies, scope, results, and trends in Carbon Footprint calculations and provides a procedure to evaluate CF on college campuses. This research shows that the most evaluated emission sources were the consumption of electrical energy (78%), transportation (74%), and the use of fuels (64%). In addition, the HEIs with the lowest emission factors for electricity consumption are Finland, England, and Colombia. Furthermore, establishing a specific carbon footprint guideline for universities would improve reports and allow better comparisons between HEIs.
ICEMS Characterization of NixFe3-xO4 (0.7 ≤ x ≤ 1.7) thin films grown by ion beam sputtering on (0001) Al2O3 substrates
Authors: Prieto P. , Prieto J. E. , Serrano A. , Soriano L. , de la Figuera J. , Marco J. F. 
Published: April 2024

AbstractWe report here on the ICEMS characterization of nickel ferrite (NixFe3−xO4) thin films having different nickel contents grown on alumina substrates by Ion Beam Sputtering. The spectra corresponding to the films with nominal x = 0.7, 1.0 and 1.2 are characteristic of compounds crystallizing in a spinel-related structure showing two different magnetic sextets associated with Fe3+ located in the tetrahedral and octahedral sites of such structure. The spectra show an additional broad third sextet with a large isomer shift which suggests the occurrence of electron hopping between Fe2+ and Fe3+ ions sitting in the octahedral sites. With increasing nickel content, the linewidth of the sextets increases and their corresponding hyperfine magnetic fields decrease. This is an indication of an increase in structural disorder in the deposited films as their nickel concentrations increase. The cation distribution of the iron ions over the tetrahedral and octahedral sites appears also to depend on the nickel content. The film with x = 1.2 shows a significant increase in the fraction of octahedral iron ions as compared with the expected nominal value suggesting that, for this composition, some Ni2+ could also occupy tetrahedral sites. The Mössbauer spectrum corresponding to the film with x = 1.7 shows a magnetic pattern with very broad lines similar to those shown by amorphous or disordered materials. The average isomer shift is quite high (around 0.40 mms− 1) and characteristic of Fe3+ in octahedral oxygen coordination. This indicates that for the largest nickel content studied (x = 1.7), the film does not contain Fe3+ in tetrahedral environments suggesting that the spinel structure is no longer present. This correlates well with the X-Ray Diffraction data which indicate a structural change from spinel to a disordered rock-salt structure for this particular film with high nickel content.
Identifying and characterising promising small molecule inhibitors of kinesin spindle protein using ligand-based virtual screening, molecular docking, molecular dynamics and MM‑GBSA calculations
Authors: Elseginy Samia A. 
Published: April 2024

AbstractThe kinesin spindle protein (Eg5) is a mitotic protein that plays an essential role in the formation of the bipolar spindles during the mitotic phase. Eg5 protein controls the segregation of the chromosomes in mitosis which renders it a vital target for cancer treatment. In this study our approach to identifying novel scaffold for Eg5 inhibitors is based on targeting the novel allosteric pocket (α4/α6/L11). Extensive computational techniques were applied using ligand-based virtual screening and molecular docking by two approaches, MOE and AutoDock, to screen a library of commercial compounds. We identified compound 8-(3-(1H-imidazol-1-ylpropylamino)-3-methyl-7-((naphthalen-3-yl)methyl)-1H-purine-2, 6 (3H,7H)-dione (compound 5) as a novel scaffold for Eg5 inhibitors. This compound inhibited cancer cell Eg5 ATPase at 2.37 ± 0.15 µM. The molecular dynamics simulations revealed that the identified compound formed stable interactions in the allosteric pocket (α4/α6/L11) of the receptor, indicating its potential as a novel Eg5 inhibitor.
Graphical Abstract
Regularised Diffusion–Shock Inpainting
Authors: Schaefer Kristina , Weickert Joachim 
Published: April 2024

AbstractWe introduce regularised diffusion–shock (RDS) inpainting as a modification of diffusion–shock inpainting from our SSVM 2023 conference paper. RDS inpainting combines two carefully chosen components: homogeneous diffusion and coherence-enhancing shock filtering. It benefits from the complementary synergy of its building blocks: The shock term propagates edge data with perfect sharpness and directional accuracy over large distances due to its high degree of anisotropy. Homogeneous diffusion fills large areas efficiently. The second order equation underlying RDS inpainting inherits a maximum–minimum principle from its components, which is also fulfilled in the discrete case, in contrast to competing anisotropic methods. The regularisation addresses the largest drawback of the original model: It allows a drastic reduction in model parameters without any loss in quality. Furthermore, we extend RDS inpainting to vector-valued data. Our experiments show a performance that is comparable to or better than many inpainting methods based on partial differential equations and related integrodifferential models, including anisotropic processes of second or fourth order.