bluuebunny/crossref_metadata_2025_split huggingface dataset:
Revealing urban area from mobile positioning data
Authors: Pintér Gergő 
Published: December 2024

AbstractResearchers face the trade-off between publishing mobility data along with their papers while protecting the privacy of the individuals. In addition to the anonymization process, other techniques, such as spatial discretization and location concealing or removal, are applied to achieve these dual objectives. The primary research question is whether concealing the observation area is an adequate form of protection or whether human mobility patterns in urban areas are inherently revealing of location. The characteristics of the mobility data, such as the number of activity records in a given spatial unit, can reveal the silhouette of the urban landscape, which can be used to infer the identity of the city in question. The presented locating method was tested on multiple cities using different open datasets and coarser spatial discretization units. While publishing mobility data is essential for research, concealing the observation area is insufficient to prevent the identification of the urban area. Instead of obscuring the observation area, noise should be added to the trajectories to mitigate privacy risks regarding the individuals.
Fuzzy APPSS: A novel method for quantifying COVID-19 impact in India under triangular spherical fuzzy environment
Authors: Sakthivel Aicevarya Devi , Augustin Felix 
Published: December 2024

AbstractIn the current scenario, decision-making models are essential for analyzing real-world problems. To address the dynamic nature of these problems, fuzzy decision-making models have been proposed by various researchers. However, an advanced technique is needed to assess uncertainty in real-time complex situations. Therefore, an association between preference and performance with satisfactory score (APPSS) method is introduced as a fuzzy decision-making method that incorporates two components: preference and performance. This method focuses on demonstrating a connection between preference and performance with a satisfactory measure. Preference analysis evaluates the significance of criteria, while performance analysis assesses the effectiveness of each alternative based on these criteria. Additionally, the satisfactory measure ensures the reliability of the outcomes. The applicability of the proposed method is demonstrated by analyzing the impact of COVID-19 on different age groups in India across various categories. The proposed method employs triangular spherical fuzzy numbers (TSFN), which is a mathematical model that extends beyond conventional fuzzy numbers by incorporating both triangular and spherical characteristics. Furthermore, a new scoring function for TSFN is developed using the graded mean integration method. The analysis reveals that the age group between 60-69 is highly vulnerable to COVID-19. The robustness of these outcomes is verified through sensitivity and comparative analyses. The findings also assist policymakers in more effectively assessing potential future health complications.
Modeling peak ground acceleration for earthquake hazard safety evaluation
Authors: Khalid Fatima , Razbin Milad 
Published: December 2024

AbstractThis paper presents a ground motion prediction (GMP) model using an artificial neural network (ANN) for shallow earthquakes, aimed at improving earthquake hazard safety evaluation. The proposed model leverages essential input variables such as moment magnitude, fault type, epicentral distance, and soil type, with the output variable being peak ground acceleration (PGA) at 5% damping. To develop this model, 885 data pairs were obtained from the Pacific Engineering Research Center, providing a robust dataset for training and validation. The ANN architecture comprises 4 nodes in the input layer, two hidden layers each containing 25 nodes, and a single-node output layer, resulting in 750 unknown weight and bias values that the model must optimize. Following the model assessment, a genetic algorithm (GA) was integrated with the ANN model to enhance its predictive capabilities. This integration aimed to forecast 20 potential earthquake scenarios, a crucial step in validating the model’s effectiveness. The results were promising, as the ANN-GA successfully predicted earthquake occurrences in 15 out of 20 scenarios. These findings underscore the model’s potential in accurately forecasting seismic events, thereby contributing to the development of more resilient infrastructure and better-informed urban planning strategies.
A precision agriculture solution for water stress estimation in Hass avocado farms in Colombia
Authors: Medina Daniel Enrique , Medina Juan David , Zorro Julio Alexis , Medina Tobon Daniel , Gomez Juan Jose , Giraldo Luis Felipe 
Published: December 2024

AbstractAgriculture 4.0 technologies continue to see low adoption among small and medium-sized farmers, primarily because these solutions often fail to account for the specific challenges of rural areas. In this work, we propose and implement a design methodology to develop a Precision Agriculture solution aimed at assisting farmers in managing water stress in Hass avocado crops. This methodology provides a structured approach for development, enabling the identification of key issues and appropriate solutions. The resulting device measures essential weather variables for calculating crop evapotranspiration and effective precipitation, operates without requiring internet or electricity connections, and transmits data globally via satellite connectivity, overcoming the limitations of existing solutions for this crop. As a result, it can detect water stress and provide crucial information for irrigation scheduling. The proposed solution was tested at a working Hass avocado farm for over a year, collecting weather data and undergoing both major and minor revisions during the iterative testing process. The collected data—covering air temperature, relative humidity, sunshine duration, and rainfall—has been made freely available to support further research and development.
Impact of COVID-19 on heart rate variability in post-COVID individuals compared to a control group
Authors: Santos-de-Araújo Aldair Darlan , Bassi-Dibai Daniela , Marinho Renan Shida , Dourado Izadora Moraes , de Almeida Lucivalda Viegas , de Sousa dos Santos Sigrid , Phillips Shane A. , Borghi-Silva Audrey 
Published: December 2024

AbstractThis study investigated the impact of mild COVID-19 on HRV in groups stratified by time after infection and to compare to a healthy group of the same age without previous virus infection and without need of hospitalization. This is a cross-sectional study. We divided the sample into four groups: control group (CG) (n = 31), group 1 (G1): ≤6 weeks (n = 34), group 2 (G2): 2–6 months (n = 30), group 3 (G3): 7–12 months (n = 35) after infection. For HRV analysis, we used the indices of linear (time and frequency domain) and non-linear analysis. For comparisons between groups, ANOVA one way test or Kruskal–Wallis was used according to the data distribution. The effect size was calculated based on Cohen’s d or η2. Simple and multiple linear regressions were performed to investigate the interaction between clinical outcomes and HRV parameters. A total of 130 individuals were included. Groups G1 and G2 showed less parasympathetic modulation when compared to CG (p < 0.05), while G3 showed an increase in parasympathetic modulation when compared to G1 (p < 0.05). Moderate to large effect sizes were found according to Cohen d or η2. The multiple linear regression models identified age and infection duration as significant predictors for RMSSD (adjusted R2 = 0.227) and SD1 (adjusted R2 = 0.242), while age was significant for SDNN (adjusted R2 = 0.213). BMI, hypertension, and dyslipidemia were non-significant in all models. For HF (n.u.), infection duration was consistently significant, with stress emerging as a predictor in Model 2 (adjusted R2 = 0.143). The recovery time since diagnosis and age influences recovery from HRV, suggesting a transient effect of the disease on the autonomic nervous system.
Finite element analysis of RC beams using static experimental data to predict static and dynamic behaviors
Authors: Sivasuriyan Arvindan , Vijayan D. S. , Sankaran Naveen , Parthiban D. 
Published: December 2024

AbstractThis study comprehensively compares dynamic and static forces in reinforced concrete (RC) beams, utilising experimental and finite element analysis (FEA) methodologies. Experimental tests involve monotonic two-point loading of 1 m x 150 mm x 150 mm RC beams using a universal testing machine (UTM). Deflection measurements are taken at three distinct locations (S1–S3) using various sensors, including force resisting sensor (FRS), flex sensor (FLS), MEMS accelerometer, and Piezoelectric sensors. The experimental data is then compared with FEA results obtained through ANSYS 16.0 software. Additionally, dynamic analysis is conducted, and results are presented in 3D graphical format. Mode shapes and harmonic responses are analysed. The study further discusses the sensor outcomes that align closely with FEA results. Overall, this research provides valuable insights into the dynamic and static behaviour of RC beams and offers a robust validation approach through experimental and computational analyses.
Detection and phylogenetic analysis of blood-associated pathogens from spleen samples of wild raccoons (Procyon lotor) in Germany
Authors: Unterköfler Maria Sophia , Schwingshandl Aria , Eigner Barbara , Pikalo Jutta , Harl Josef , Spergser Joachim , Steinbach Peter , Jeschke Diana , Striese Michael , Striese Elisabeth , Ansorge Hermann , Fuehrer Hans-Peter , Heddergott Mike 
Published: December 2024

AbstractRaccoons (Procyon lotor) originated in North America and have been introduced to Europe. Due to their close contact with human settlements, they are important reservoirs for zoonotic pathogens, such as Baylisascaris procyonis. The relevance and prevalence of vector-borne pathogens have not yet been fully elucidated. In this study, we screened 285 spleen samples of raccoons, collected between 2019 and 2022 in Germany. The samples were analysed by PCR to detect Mycoplasma spp., Anaplasmataceae, Bartonella spp., Babesia spp., Rickettsia spp., Filarioidea, Trypanosomatida and Hepatozoon spp., and positive PCR products were sequenced. In total, 104 samples were positive for Mycoplasma spp. (36.49%), making this the first study to detect Mycoplasma spp. in raccoons outside of North America. Three samples were positive for Babesia spp. (1.05%) and two for Anaplasma phagocytophilum (0.7%). Phylogenetic analysis revealed that the Mycoplasma spp. detected all belong to the haemotrophic mycoplasmas cluster and were grouped within a single phylogenetic clade. Two different Babesia spp. were detected, one of which was closely related to Babesia canis, while the other was more closely related to Babesia sp. from ruminants. It is unclear whether the pathogens detected have an impact on the health of raccoons or whether they may serve as a reservoir for other animals.
Impact of gut colonization by antibiotic-resistant bacteria on the outcomes of autologous stem cell transplantation in multiple myeloma
Authors: Jasiński Marcin , Biliński Jarosław , Maciejewska Martyna , Ostrowska Karolina , Rusicka-Krzewska Patrycja , Konarski Wojciech , Podsiadły Edyta , Snarski Emilian , Basak Grzegorz W. 
Published: December 2024

AbstractPatients undergoing autologous stem cell transplantation (auto-SCT) face elevated risks of infections. Additionally, patients colonized in the gastrointestinal tract with antibiotic-resistant bacteria (ARB) are at higher risk of infection with ARB and other infections. Therefore, patients colonized with ARB before auto-SCT should present with an exceptionally high incidence of infections. According to current literature, ARB colonization is the surrogate marker for dysbiosis, which is known to be associated with a diagnosis of multiple myeloma (MM). Given that, this retrospective study aimed to assess the influence of ARB colonization on infection rates, hematopoiesis regeneration, mucositis, overall survival, and progression-free survival following auto-SCT in MM. Data from 138 MM patients undergoing 141 auto-SCT were analyzed, with 15% showing ARB colonization. Among colonized patients, ESBL-producing gram-negative rods predominated. Patients with gut ARB colonization had significantly higher infection rates than non-colonized individuals (52 vs. 26%, P = 0.02), particularly bloodstream infections (43% vs. 14%, P = 0.004). Colonized patients also tended to exhibit shorter survival rates although there was no statistical significance (1-year and 2-year OS; non-colonized vs. colonized; 97 and 92% vs. 90 and 86%; p = 0.054). Based on our results, gut colonization before auto-SCT negatively affects treatment outcomes.
Automatic scent creation by cheminformatics method
Authors: Aleixandre Manuel , Prasetyawan Dani , Nakamoto Takamichi 
Published: December 2024

AbstractThe sense of smell is fundamental for various aspects of human existence including the flavor perception, environmental awareness, and emotional impact. However, unlike other senses, it has not been digitized. Its digitalization faces challenges such as the lack of reliable odor sensing technology or the precise scent delivery through olfactory displays. Its subjective nature and context dependence add complexity to the process. Moreover, the method of converting odors to digital information remains unclear. This work focuses on one of the most challenging aspects of digital olfaction: automatic scent creation. We propose a method that automatically creates a desired odor profile with the addition of one specific odor descriptor. It is based on a deep neural network that predicts odor descriptors from the multidimensional sensing data, such as mass spectra and an odor reproduction technique using odor components. The results demonstrate that the proposed method can successfully create a scent with the desired odor profile and that its performance depends on the accuracy of the underlying odor predicting method. This opens up the possibility of automatic scent creation, allowing for the presentation of scents with specific odor profiles with an olfactory display.
Implications of cosmologically coupled black holes for pulsar timing arrays
Authors: Calzà Marco , Gianesello Francesco , Rinaldi Massimiliano , Vagnozzi Sunny 
Published: December 2024

AbstractIt has been argued that realistic models of (singularity-free) black holes (BHs) embedded within an expanding Universe are coupled to the large-scale cosmological dynamics, with striking consequences, including pure cosmological growth of BH masses. In this pilot study, we examine the consequences of this growth for the stochastic gravitational wave background (SGWB) produced by inspiraling supermassive cosmologically coupled BHs. We show that the predicted SGWB amplitude is enhanced relative to the standard uncoupled case, while maintaining the $$\Omega _{\text {gw}} \propto f^{2/3}$$ frequency scaling of the spectral energy density. For the case where BH masses grow with scale factor as $$M_{\text {bh}} \propto a^3$$, thus contributing as a dark energy component to the cosmological dynamics, $$\Omega _{\text {gw}}$$ can be enhanced by more than an order of magnitude. This has important consequences for the SGWB signal detected by pulsar timing arrays, whose measured amplitude is slightly larger than most theoretical predictions for the spectrum from inspiraling binary BHs, a discrepancy which can be alleviated by the cosmological mass growth mechanism.
Optimization of microwave components using machine learning and rapid sensitivity analysis
Authors: Koziel Slawomir , Pietrenko-Dabrowska Anna 
Published: December 2024

AbstractRecent years have witnessed a tremendous popularity growth of optimization methods in high-frequency electronics, including microwave design. With the increasing complexity of passive microwave components, meticulous tuning of their geometry parameters has become imperative to fulfill demands imposed by the diverse application areas. More and more often, achieving the best possible performance requires global optimization. Unfortunately, global search is an intricate undertaking. To begin with, reliable assessment of microwave components involves electromagnetic (EM) analysis entailing significant CPU expenses. On the other hand, the most widely used nature-inspired algorithms require large numbers of system simulations to yield a satisfactory design. The associated costs are impractically high if not prohibitive. The use of available mitigation methods, primarily surrogate-based approaches, is impeded by dimensionality-related problems and the complexity in microwave circuit characteristics. This research introduces a procedure for expedited globalized parameter adjustment of microwave passives. The search process is embedded in a surrogate-assisted machine learning framework that operates in a dimensionality-restricted domain, spanned by the parameter space directions being of importance in terms of their effects on the circuit characteristic variability. These directions are established using a fast global sensitivity analysis procedure developed for this purpose. Domain confinement reduces the cost of surrogate model establishment and improves its predictive power. The global optimization phase is complemented by local tuning. Verification experiments demonstrate the remarkable efficacy of the presented approach and its advantages over the benchmark methods that include machine learning in full-dimensionality space and population-based metaheuristics.
Evaluation of overwintering risk of tropical and subtropical insect pests in temperate regions
Authors: Matsukura Keiichiro , Mizutani Nobuo , Tanaka Sayumi , Tanaka Yoshiaki 
Published: December 2024

AbstractRecent changes in climate and environments have promoted the range expansion of insect pests of tropical and subtropical origins into temperate regions. For more accurate and faster risk assessment of this expansion, we developed a novel indicator to link a physiologically derived parameter of chilling injury with the survival of insect populations in nature by using two insects, Spodoptera frugiperda and Cicadulina bipunctata with tropical and subtropical origins, and one cool-adapted insect, Laodelphax striatellus. The parameter derived from a proportional increment in the time to 99.9% mortality under constant low temperatures causing chilling injury evaluates the survival of target insect populations based on winter climate data. For S. frugiperda and C. bipunctata, but not for L. striatellus, the accuracy of the model in predicting the overwintering range was equivalent to, or better than, those of a conventional species distribution model. Additional field testing using S. frugiperda and comparison of the developed model with a conventional logistic model for C. bipunctata supported the validity of the indicator. These results suggest that the developed indicator will help for simple risk assessment of tropical and subtropical insect pests in temperate regions by the species distribution modelling approach.
Utility of quantitative measurement of T2 using restriction spectrum imaging for detection of clinically significant prostate cancer
Authors: Rojo Domingo Mariluz , Conlin Christopher C. , Karunamuni Roshan , Ollison Courtney , Baxter Madison T. , Kallis Karoline , Do Deondre D. , Song Yuze , Kuperman Joshua , Shabaik Ahmed S. , Hahn Michael E. , Murphy Paul M. , Rakow-Penner Rebecca , Dale Anders M. , Seibert Tyler M. 
Published: December 2024

AbstractThe Restriction Spectrum Imaging restriction score (RSIrs) has been shown to improve the accuracy for diagnosis of clinically significant prostate cancer (csPCa) compared to standard DWI. Both diffusion and T2 properties of prostate tissue contribute to the signal measured in DWI, and studies have demonstrated that each may be valuable for distinguishing csPCa from benign tissue. The purpose of this retrospective study was to (1) determine whether prostate T2 varies across RSI compartments and in the presence of csPCa, and (2) evaluate whether csPCa detection with RSIrs is improved by acquiring multiple scans at different TEs to measure compartmental T2 (cT2). Data includes two cohorts scanned for csPCa with 3T multi-b-value diffusion-weighted sequences acquired at multiple TEs. cT2 values were computed from multi-TE RSI data and compared by compartment. CsPCa detection was compared between RSIrs and a logistic regression model (LRM) to predict the probability of csPCa using cT2 in combination with RSI measurements. Two-sample t-tests (α = 0.05) and the area under the receiver operating characteristic curve (AUC) were used for the statistical analyses. In both cohorts, T2 was different (p < 0.05) across the four RSI compartments (C1, C2, C3, C4). Voxel-level, cohort 1: T2 was different in csPCa for C1, C2, C3 (p < 0.001). Patient-level, cohort 1: T2 was different in csPCa patients in C3 (p = 0.02); cohort 2: T2 differed in csPCa patients in C1 (p = 0.01), C3 (p = 0.01) and C4 (p < 0.01). Voxel-level csPCa detection: cT2 did not improve discrimination over RSIrs alone (p = 0.9). Patient-level: RSIrs and the LRM performed better than diffusion alone (p < 0.001), but the difference in AUCs between RSIrs and the LRM was not significantly different (p = 0.54). In conclusion, significant differences in cT2 were observed between normal and cancerous prostatic tissue. With our data, however, consideration of cT2 in addition to diffusion did not significantly improve cancer detection performance.
Genome-wide analysis of alternative splicing differences in hepatic ischemia reperfusion injury
Authors: Hua Yongliang , Li Xinglong , Yin Bing , Lu Shounan , Qian Baolin , Zhou Yongzhi , Li Zhongyu , Meng Zhanzhi , Ma Yong 
Published: December 2024

AbstractAlternative splicing (AS) contributes to transcript and protein diversity, affecting their structure and function. However, the specific transcriptional regulatory mechanisms underlying AS in the context of hepatic ischemia reperfusion (IR) injury in mice have not been extensively characterized. In this study, we investigated differentially alternatively spliced (DAS) genes and differentially expressed transcripts (DETs) in a mouse model of hepatic IR injury using the high throughput RNA sequencing (RNA-seq) analysis and replicate multivariate analysis of transcript splicing (rMATS) analysis. We further conducted Gene ontology (GO) term enrichment, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the protein-protein interaction (PPI) network. A total of 898 DAS genes (p ≤ 0.05) were screened out in the hepatic IR group compared to the sham group, while functional enrichment analysis revealed that DETs and DAS genes were significantly associated with the ATP-dependent chromain, splicesome and metabolic pathways. The expression level of the DAS genes: Gabpb2, Smg1, Tnrc6c, Mettl17, Smpd4, Kcnt2, D16Ertd472e, Rab3gap2, Echdc2 and Ssx2ip were verified by RT-PCR and qRT-PCR. Our findings provide a comprehensive genome-wide view of AS events in hepatic IR injury in mice, enhancing our understanding of AS dynamics and the molecular mechanisms governing alternative pre-mRNA splicing.